We investigate the cooperation among energy prosumers (unified energy provider and consumer) through the energy packet network (EPN) paradigm, which represents both the flow of work that requires energy, and the flow of energy itself, in terms of discrete units. This paper details a stochastic model of EPNs, which is inspired from a branch of queuing theory called G-networks. The model allows us to compute the equilibrium state of a system that includes energy storage units, energy transmission networks, and energy consumers, together with the intermittent energy sources. The model is then used to show how the flow of work and energy in the system can be optimized for certain utility functions that consider both the needs of the consumers, and the desire to maintain some reserve energy for potential future needs.
a2169-3536 01493nas a2200169 4500000000100000000000100001008004100002260001200043100001100055700001100066700001700077245005300094300001200147490000700159520114200166022001501308 2016 d c02/2016 1 aO Brun 1 aL Wang 1 aErol Gelenbe 00 aBig Data for Autonomic Intercontinental Overlays a575-583 0 v34 3 aThis paper uses big data and machine learning for the real-time management of Internet scale quality-of-service (QoS) route optimisation with an overlay network. Based on the collection of data sampled every 2 min over a large number of source-destinations pairs, we show that intercontinental Internet protocol (IP) paths are far from optimal with respect to QoS metrics such as end-to-end round-trip delay. We, therefore, develop a machine learning-based scheme that exploits large scale data collected from communicating node pairs in a multihop overlay network that uses IP between the overlay nodes, and selects paths that provide substantially better QoS than IP. Inspired from cognitive packet network protocol, it uses random neural networks with reinforcement learning based on the massive data that is collected, to select intermediate overlay hops. The routing scheme is illustrated on a 20-node intercontinental overlay network that collects some 2 × 106 measurements per week, and makes scalable distributed routing decisions. Experimental results show that this approach improves QoS significantly and efficiently
a0733-8716 01217nas a2200157 4500000000100000000000100001008004100002260002600043100001200069700001700081245005600098300000900154490000800163520086600171022002201037 2016 d bIEEE aBrunów, Poland 1 aL. Wang 1 aErol Gelenbe 00 aReal-Time Traffic over the Cognitive Packet Network a3-21 0 v608 3 aReal-Time services over IP (RTIP) have been increasingly significant due to the convergence of data networks worldwide around the IP standard, and the popularisation of the Internet. Real-Time applications have strict Quality of Service (QoS) constraint, which poses a major challenge to IP networks. The Cognitive Packet Network (CPN) has been designed as a QoS-driven protocol that addresses user-oriented QoS demands by adaptively routing packets based on online sensing and measurement, and in this paper we design and experimentally evaluate the“Real-Time (RT) over CPN” protocol which uses QoS goals that match the needs of real-time packet delivery in the presence of other background traffic under varied traffic conditions. The resulting design is evaluated via measurements of packet delay, delay variation (jitter) and packet loss ratio.
a978-3-319-39206-6 01151nas a2200145 4500000000100000000000100001008004100002260003300043100001600076700001700092245006900109300000800178520079700186022002200983 2016 d bIEEE aKuala Lumpur, Malaysia 1 aF. Francois 1 aErol Gelenbe 00 aTowards a cognitive routing engine for software defined networks a1-6 3 aIn Software Defined Networks (SDN), intensive traffic monitoring is used to optimize the Quality-of-Service (QoS) of the network paths which are selected. Thus, we introduce the use of the Cognitive Packet Network (CPN) algorithm to SDN in order to optimize the search for new high-QoS paths. We install the CPN algorithm in the Cognitive Routing Engine (CRE), a new application software for SDN, and show that with limited monitoring overhead we are able to determine the near-optimal paths for given QoS metrics that may be proposed by the end users. Measurements that we have conducted on an experimental replica of the GEANT network that our approach uses close to 10 times less monitoring data than conventional SDN, but that we are able to approach the optimal paths within 2%.
a978-1-4799-6664-6 00982nas a2200157 4500000000100000000000100001008004100002260002800043100001700071700002100088700001700109245006800126300000800194520060000202020002200802 2016 d bIEEE aKauai, Hawai, USA 1 aErol Gelenbe 1 aOmer Abdelrahman 1 aGokce Gorbil 00 aDetection and mitigation of signaling storms in mobile networks a1-5 3 aMobile Networks are subject to “signaling storms” launched by malware or apps, which overload the the bandwidth at the cell, the backbone signaling servers, and Cloud servers, and may also deplete the battery power of mobile devices. This paper
reviews the subject and discusses a novel technique to detect and mitigate such signaling storms. Through a mathematical analysis we introduce a technique based on tracking time-out transitions in the signaling system that can substantially reduce both the number of misbehaving mobiles and the signaling overload in the backbone.
Even web services that are free of charge, typically offer access to online information based on some form of economic interest of the web service itself. Thus advertisers who put the information on the web will make a payment to the search services based on the clicks that their advertisements receive. Thus end users cannot know that the results they obtain from Web search engines are exhaustive, or that they actually respond to their needs. To fill the gap between user needs and the information that is presented to them on the web, Intelligent Search Assistants have been proposed to act at the interface between users and search engines to present data to users in a manner that reflects their actual needs or their observed or stated preferences. This paper presents an Intelligent Internet Search Assistant based on the Random Neural Network that tracks the user’s preferences and makes a selection on the output of one or more search engines using the preferences that it has learned. We also introduce a “relevance metric” to compare the performance of our Intelligent Internet Search Assistant against a few search engines, showing that it provides better performance.
a78-3-319-44943-2 01397nas a2200145 4500000000100000000000100001008004100002260004800043100002100091700001700112245005700129300001200186520103100198020002201229 2016 d bIEEE aImperial Coll Campus, London, England 1 aOmer Abdelrahman 1 aErol Gelenbe 00 aA Diffusion Model for Energy Harvesting Sensor Nodes a154-158 3 aEnergy harvesting has recently attracted much interest due to the emergence of the Internet of Things, and the increasing need to operate wireless sensing devices in challenging environments without much human intervention and maintenance. This paper presents a novel approach for modeling the performance of an energy harvesting wireless sensor node, which takes into account fluctuations in the amount of energy extracted from the environment, energy loss due to battery leakage, as well as the energy cost of sensing, data processing and communication. The proposed approach departs from the common queueing-theoretic framework used in the literature, and instead uses Brownian motion to represent more accurately the time evolution of the distribution of the node's battery level. The paper derives some performance measures of interest along with the stationary solution of the system, and discusses possible directions for reducing the number of parameters and states of the model without compromising accuracy.
a978-1-5090-3432-1 01533nas a2200145 4500000000100000000000100001008004100002260004800043100001600091700001700107245009500124300001200219520113400231022002201365 2016 d bIEEE aImperial Coll Campus, London, England 1 aF. Francois 1 aErol Gelenbe 00 aOptimizing Secure SDN-Enabled Inter-Data Centre Overlay Networks through Cognitive Routing a283-288 3 aMore and more businesses are deploying their application(s) with different cloud providers which are close to their customers in order to provide better Quality of Service (QoS) to their end customers. In this work, an optimized and secure software-defined overlay network is proposed as an efficient mechanism to interconnect these geographically-dispersed applications compared to using only plain IP routing. A logically centralized Cognitive Routing Engine (CRE), based on Random Neural Networks with Reinforcement Learning, was developed to find with minimal monitoring overhead the optimal overlay paths when the public Internet is used as the communication means between the overlay nodes. CRE was evaluated by using an overlay network composed of hosts from 5 different public clouds where it was shown that the latency of CRE paths is most of the time within 5% of the latency of the optimal IP paths. Furthermore, it was also demonstrated that CRE is able to do asymmetric path optimization where the forward path is different from the reverse path for a given data centre pair in order to further improve QoS.
a978-1-5090-3432-1 01151nas a2200145 4500000000100000000000100001008004100002260003700043100001700080700001000097245004600107300001400153520081600167022002200983 2016 d c06/2016 bIEEE aVancouver, Canada 1 aErol Gelenbe 1 aY Yin 00 aDeep learning with random neural networks a1633-1638 3 aThis paper introduces techniques for Deep Learning in conjunction with spiked random neural networks that closely resemble the stochastic behaviour of biological neurons in mammalian brains. The paper introduces clusters of such random neural networks and obtains the characteristics of their collective behaviour. Combining this model with previous work on extreme learning machines, we develop multilayer architectures which structure Deep Learning Architectures a a “front end” of one or two layers of random neural networks, followed by an extreme learning machine. The approach is evaluated on a standard - and large - visual character recognition database, showing that the proposed approach can attain and exceed the performance of techniques that were previously reported in the literature.
a978-1-5090-0620-5 00993nas a2200157 4500000000100000000000100001008004100002260002800043100001100071700001100082700001700093245006300110300001400173520062500187022002300812 2016 d bIEEE aBudapest, Hungary 1 aL Wang 1 aO Brun 1 aErol Gelenbe 00 aAdaptive workload distribution for local and remote Clouds a3984-3988 3 aCloud systems include both locally based servers at user premises and remote servers and multiple Clouds that can be reached over the Internet. This paper describes a smart distributed system that combines local and remote Cloud facilities. It operates with a task allocation system that takes decisions to allocate tasks dynamically to the service that offers the best overall Quality of Service and a routing overlay which optimizes network delay for data transfer between clouds. Internet-scale experiments exhibit the effectiveness of our approach in adaptively distributing workload across multiple clouds.
a978-1-5090-1897-0 01332nas a2200145 4500000000100000000000100001008004100002260001200043100001700055245004300072300001000115490000700125520104000132022001401172 2016 d c09/2016 1 aErol Gelenbe 00 aAgreement in Spins and Social Networks a15-17 0 v44 3 aWe introduce a model of mutually influencing individuals who hold one of two opinions about some matter. This is similar to a system of particles that can have one of two spin states. The model considers N sub-systems where individuals stay for some time, and then move from one subnetwork to another independently of each other, or they may leave the overall network from any one of the sub-systems. They arrive externally to any sub-system according to independent Poisson processes. In each sub-system individuals can influence each other to align with their own opinion or spin, and their opinion can also fluctuate at random in either opinion. The system also allows for a bias or directional field in any of the sub-systems that influences the individuals or spins that are locally present. We show that even with a weak bias, when random fluctuations become small then all the individuals or spins in a given sub-network will align with probability one, to the opinion or spin direction represented by the bias or spin.
a0163-5999 01593nas a2200169 4500000000100000000000100001008004100002100001700043700002100060700001600081700001700097245007200114300001200186490000600198520120500204022001401409 2016 d 1 aGokce Gorbil 1 aOmer Abdelrahman 1 aM. Pavloski 1 aErol Gelenbe 00 aModeling and Analysis of RRC-Based Signalling Storms in 3G Networks a113-127 0 v4 3 aMobile networks are vulnerable to signaling attacks and storms that are caused by traffic patterns that overload the control plane, and differ from distributed denial of service attacks in the Internet since they directly affect the control plane, and also reserve wireless bandwidth and network resources without actually using them. Such storms can result from malware and mobile botnets, as well as from poorly designed applications, and can cause service outages in 3G and 4G networks, which have been experienced by mobile operators. Since the radio resource control (RRC) protocol in the 3G and 4G networks is particularly susceptible to such storms, we analyze their effect with a mathematical model that helps to predict the congestion that is caused by a storm. A detailed simulation model of a mobile network is used to better understand the temporal dynamics of user behavior and signaling in the network and to show how RRC-based signaling attacks and storms cause significant problems in both the control and user planes of the network. Our analysis also serves to identify how storms can be detected, and to propose how system parameters can be chosen to mitigate their effect.
a2168-6750 00457nam a2200169 4500000000100000000000100001008004100002260001300043100002100056700001700077700001700094700001700111245012100128300000800249490000800257020002200265 2016 d bSpringer 1 aOmer Abdelrahman 1 aErol Gelenbe 1 aGokce Gorbil 1 aRicardo Lent 00 aInformation Sciences and Systems 2015 30th International Symposium on Computer and Information Sciences (ISCIS 2015) a457 0 v363 a978-3-319-22634-7 00437nas a2200181 4500000000100000000000100001008004100002260000800043100001000051700001700061700001300078700001300091700001100104245010500115300001400220490000700234022001400241 2017 d cNov 1 aJ. Du 1 aErol Gelenbe 1 aC. Jiang 1 aH. Zhang 1 aY. Ren 00 aContract Design for Traffic Offloading and Resource Allocation in Heterogeneous Ultra-Dense Networks a2457-2467 0 v35 a0733-8716 00364nas a2200145 4500000000100000000000100001008004100002260004400043100001100087700001700098245006200115300001000177490000900187022002200196 2017 d c05/2017 bIEEE aAnchorage, United States 1 aY. Yin 1 aErol Gelenbe 00 aSingle-cell based random neural network for deep learning a86-93 0 v2017 a978-1-5090-6182-2 00439nas a2200157 4500000000100000000000100001008004100002260004400043100001100087700001200098700001700110245010900127300001400236490000900250022002200259 2017 d c05/2017 bIEEE aAnchorage, United States 1 aY. Yin 1 aL. Wang 1 aErol Gelenbe 00 aMulti-layer neural networks for quality of service oriented server-state classification in cloud servers a1623-1627 0 v2017 a978-1-5090-6182-2 00359nas a2200157 4500000000100000000000100001008004100002260001200043100001700055700001900072700001700091245006000108300001200168490000700180022001400187 2017 d c06/2017 1 aH. Liu Jiang 1 aRK. Thulasiram 1 aErol Gelenbe 00 aSpecial Issue on Green Pervasive and Ubiquitous Systems a806-812 0 v11 a1932-8184 00299nas a2200121 4500000000100000000000100001008004100002260003000043100001800073700001700091245004900108020002000157 2017 d c2017 bIEEE aParis, France 1 aY.M. Kadioglu 1 aErol Gelenbe 00 aWireless sensor with data and Energy Packets a978-150901897-0 00356nas a2200133 4500000000100000000000100001008004100002260004600043100001800089700001700107700001700124245005800141020002300199 2017 d c2017 bSpringer aKazan, Russian Federation 1 aE. Pankratova 1 aM. Farkhadov 1 aErol Gelenbe 00 aResearch of Heterogeneous Queueing System SM|M(n)|∞ a978-3-319-68068-2 00289nas a2200145 4500000000100000000000100001008004100002260001200043100001500055700001700070245002700087300000800114490000600122022001500128 2017 d c07/2017 1 aJ Fourneau 1 aErol Gelenbe 00 aG-networks with Adders a1-7 0 v9 a1999-5903 00346nas a2200145 4500000000100000000000100001008004100002260001200043100001500055700001700070245007700087300001300164490000800177022001500185 2018 d c03/2018 1 aW. Serrano 1 aErol Gelenbe 00 aThe Random Neural Network in a neurocomputing application for Web search a123-134 0 v280 a0925-2312 00309nas a2200145 4500000000100000000000100001008004100002260001200043100001200055700001700067245004700084300001100131490000600142022001500148 2018 d c03/2018 1 aL. Wang 1 aErol Gelenbe 00 aAdaptive Dispatching of Tasks in the Cloud a33-45 0 v6 a2168-7161 00303nas a2200121 4500000000100000000000100001008004100002260003200043100001500075700001700090245005100107020002300158 2017 d c03/2018 bIEEE aLondon, UK 1 aW. Serrano 1 aErol Gelenbe 00 aIntelligent search with deep learning clusters a978-1-5090-6435-9 00312nas a2200121 4500000000100000000000100001008004100002260004500043100001700088700001000105245005200115020002300167 2017 d c09/2017 bSpringer, Cham aKraków, Poland 1 aErol Gelenbe 1 aY Yin 00 aDeep Learning with Dense Random Neural Networks a978-3-319-67792-7 00336nas a2200121 4500000000100000000000100001008004100002260004600043100001500089700001700104245007000121020002300191 2017 d c2017 bSpringer, Cham aVilamoura, Portugal 1 aW. Serrano 1 aErol Gelenbe 00 aThe Deep Learning Random Neural Network with a Management Cluster a978-3-319-59423-1 00441nas a2200169 4500000000100000000000100001008004100002260004100043100000900084700001700093700001200110700001200122700001000134700001000144245009400154020002300248 2017 d c01/2018 bIEEE aSingapore, Singapore 1 aJ Du 1 aErol Gelenbe 1 aC Jiang 1 aH Zhang 1 aZ Han 1 aY Ren 00 aData Transaction Modeling in Mobile Networks: Contract Mechanism and Performance Analysis a978-1-5090-5019-2 01633nas a2200217 4500000000100000000000100001008004100002260001200043100002200055700002000077700002000097700002000117700002400137700001700161700001700178245006300195300001000258490000800268520112500276022001401401 2018 d c08/2018 1 aMariacarla Staffa 1 aLuigi Sgaglione 1 aGiovanni Mazzeo 1 aLuigi Coppolino 1 aSalvatore D'Antonio 1 aLuigi Romano 1 aErol Gelenbe 00 aAn OpenNCP-based Solution for Secure eHealth Data Exchange a65-85 0 v116 3 aThe European Commission is very focused on the development of possible solutions to allow effective cross border healthcare provisioning with the aim of guaranteeing a uniform Quality of Service (QoS) level of healthcare systems across Europe. One of the most relevant efforts in this direction was the epSOS Project, with the release of the OpenNCP platform overcoming interoperability issues in health information exchange between nations while complying with both National and European laws. Despite the OpenNCP platform has been adopted in several Member States, allowing them to securely interconnect their eHealth infrastructures, some security issues are still partially addressed, This work shows how the KONFIDO project will address these issues by building on and extending the results of OpenNCP through a sound holistic approach to security at a systemic level. This paper describes in detail the KONFIDO project approach and how it is being deployed via a combination of complementary security enhancing technologies with the ultimate goal of increasing trust and security of eHealth data exchange.
a1084-8045 01743nas a2200157 4500000000100000000000100001008004100002260001200043100001900055700001700074245007200091300000900163490000700172520139100179022001501570 2018 d c08/2018 1 aYasin Kadioglu 1 aErol Gelenbe 00 aProduct-Form Solution for Cascade Networks With Intermittent Energy a1-10 0 vPP 3 aThe power needs of digital devices, their installation in locations where it is difficult to connect them to the power grid and the difficulty of frequently replacing batteries, create the need to operate digital systems with harvested energy. In such cases, local storage batteries must overcome the intermittent nature of the energy supply. System performance then depends on the intermittent energy supply, possible energy leakage, and system workload. Queueing networks with product-form solution (PFS) are standard tools for analyzing the performance of interconnected systems, and predicting relevant performance metrics including job queue lengths, throughput, and system turnaround times and delays. However, existing queueing network models assume unlimited energy availability, whereas intermittently harvested energy can affect system performance due to insufficient energy supply. Thus, this paper develops a new PFS for the joint probability distribution of energy availability, and job queue length for an N-node tandem system. Such models can represent production lines in manufacturing systems, supply chains, cascaded repeaters for optical links, or a data link with multiple input data ports that feeds into a switch or server. Our result enables the rigorous computation of the relevant performance metrics of such systems operating with intermittent energy.
a1937-9234 01501nas a2200133 4500000000100000000000100001008004100002260004100043100001700084700001900101245007500120520114900195020002301344 2018 d c07/2018 bIEEE aKansas City, MO, USA 1 aErol Gelenbe 1 aYasin Kadioglu 00 aEnergy life-time of wireless nodes with network attacks and mitigation 3 aIn the Internet of Things (IoT), a simple form of attack can deplete the energy available to operate the sensor nodes. Some of these nodes may use batteries, while others may harvest ambient energy such as photovoltaic, or electromagnetic, or vibration based energy. We first briefly survey the types of attacks which aim at the nodes' energy provisioning systems. This paper analyses the effect of such attacks on the energy life- time of a wireless node. Then we provide models to estimate the effect of attacks that attempt to deplete the node's energy supply, both for a node that uses energy harvesting. We then examine a simple means of attack mitigation based on dropping both attack and «good» traffic. For nodes that use energy harvesting, we compute the fraction of traffic that must be dropped so as to offer a desired «energy life-time» of the node. We see that the required traffic drop rate depends in a non-linear manner on the nominal «good traffic rate» at which the node is expected to operate. Finally, we analyse the impact of attacks on the energy life-time of a node that operates with a replaceable battery
a978-1-5386-4328-0 00454nas a2200145 4500000000100000000000100001008004100002260004400043100001700087700001600104700001700120245011300137520004400250020001400294 2018 d c07/2018 bElsevier aGran Canaria, Spain 1 aOlivier Brun 1 aYonghua Yin 1 aErol Gelenbe 00 aDeep Learning with Dense Random Neural Network for Detecting Attacks against IoT-connected Home Environments 3 a a1877-0509 01382nas a2200193 4500000000100000000000100001008004100002260003700043100002100080700001700101700002400118700002200142700002300164245009800187300001200285490000800297520086300305020002001168 2018 d bSpringer aLondon, United Kingdom 1 aJoanna Domańska 1 aErol Gelenbe 1 aTadeusz Czachórski 1 aAnastasios Drosou 1 aDimitrios Tzovaras 00 aResearch and innovation action for the security of the internet of things: The SerIoT project a101-118 0 v821 3 aThe Internet of Things (IoT) was born in the mid 2010’s, when the threshold of connecting more objects than people to the Internet, was crossed. Thus, attacks and threats on the content and quality of service of the IoT platforms can have economic, energetic and physical security consequences that go way beyond the traditional Internet’s lack of security, and way beyond the threats posed by attacks to mobile telephony. Thus, this paper describes the H2020 project “Secure and Safe Internet of Things” (SerIoT) which will optimize the information security in IoT platforms and networks in a holistic, cross-layered manner (i.e. IoT platforms and devices, honeypots, SDN routers and operator’s controller) in order to offer a secure SerIoT platform that can be used to implement secure IoT platforms and networks anywhere and everywhere.
a978-331995188-1 01021nas a2200181 4500000000100000000000100001008004100002260004600043100001700089700001600106700001700122700002200139700002800161700001700189245011400206520049900320020002000819 2018 d c07/2018 bSpringer aLondon, United Kingdom 1 aOlivier Brun 1 aYonghua Yin 1 aErol Gelenbe 1 aY. Murat Kadioglu 1 aJavier Augusto-Gonzalez 1 aManuel Ramos 00 aDeep learning with dense random neural networks for detecting attacks against IoT-connected home environments 3 aIn this paper, we analyze the network attacks that can be launched against IoT gateways, identify the relevant metrics to detect them, and explain how they can be computed from packet captures. We also present the principles and design of a deep learning-based approach using dense random neural networks (RNN) for the online detection of network attacks. Empirical validation results on packet captures in which attacks were inserted show that the Dense RNN correctly detects attacks.
a978-331995188-1 01430nas a2200205 4500000000100000000000100001008004100002260004600043100002200089700002000111700002000131700001700151700002000168700002100188700001400209700001900223245006600242520089400308020002201202 2018 d c07/2018 bSpringer aLondon, United Kingdom 1 aMariacarla Staffa 1 aLuigi Coppolino 1 aLuigi Sgaglione 1 aErol Gelenbe 1 aIoannis Komnios 1 aEvangelos Grivas 1 aOana Stan 1 aLuigi Castaldo 00 aKONFIDO: An OpenNCP-Based Secure eHealth Data Exchange System 3 aAllowing cross-border health-care data exchange by establishing a uniform QoS level of health-care systems across European states, represents one of the current main goals of the European Commission. For this purpose epSOS project was funded with the objective to overcome interoperability issues in patients health information exchange among European healthcare systems. A main achievement of the project was the OpenNCP platform. Settled over the results of the epSOS project, KONFIDO aims at increasing trust and security of eHealth data exchange by adopting a holistic approach, as well as at increasing awareness of security issues among the healthcare community. In this light, the paper describes the KONFIDO project’s approach and discusses its design and its representation as a system of interacting agents. It finally discusses the deployment of the provided platform
a978-3-319-95189-8 01797nas a2200157 4500000000100000000000100001008004100002260004600043100002200089700001700111700002300128700002300151245006900174520137400243020002201617 2018 d c07/2018 bSpringer aLondon, United Kingdom 1 aMiltiadis Siavvas 1 aErol Gelenbe 1 aDionysios Kehagias 1 aDimitrios Tzovaras 00 aStatic Analysis-Based Approaches for Secure Software Development 3 aSoftware security is a matter of major concern for software development enterprises that wish to deliver highly secure software products to their customers. Static analysis is considered one of the most effective mechanisms for adding security to software products. The multitude of static analysis tools that are available provide a large number of raw results that may contain security-relevant information, which may be useful for the production of secure software. Several mechanisms that can facilitate the production of both secure and reliable software applications have been proposed over the years. In this paper, two such mechanisms, particularly the vulnerability prediction models (VPMs) and the optimum checkpoint recommendation (OCR) mechanisms, are theoretically examined, while their potential improvement by using static analysis is also investigated. In particular, we review the most significant contributions regarding these mechanisms, identify their most important open issues, and propose directions for future research, emphasizing on the potential adoption of static analysis for addressing the identified open issues. Hence, this paper can act as a reference for researchers that wish to contribute in these subfields, in order to gain solid understanding of the existing solutions and their open issues that require further research.
a978-3-319-95189-8 00373nas a2200145 4500000000100000000000100001008004100002260002200043100001700065700002100082700002400103700002100127700002300148245005600171 2018 d bIEEE aRome, Italy 1 aErol Gelenbe 1 aJoanna Domańska 1 aTadeusz Czachórski 1 aAnastasis Drosou 1 aDimitrios Tzovaras 00 aSecurity for Internet of Things: The SerIoT Project 00225nas a2200097 4500000000100000000000100001008004100002100001700043700001800060245004900078 2019 d 1 aErol Gelenbe 1 aYunxiao Zhang 00 aPerformance Optimization With Energy Packets 00316nas a2200121 4500000000100000000000100001008004100002260001200043100001900055700002200074700001700096245008100113 2019 d c05/2019 1 aKhaled Hussain 1 aMohamed Bassyouni 1 aErol Gelenbe 00 aAccurate, Energy-efficient Classification With Spiking Random Neural Network 01212nas a2200133 4500000000100000000000100001008004100002260001200043100002200055700001700077245004800094490000700142520092900149 2019 d c07/2019 1 aMiltiadis Siavvas 1 aErol Gelenbe 00 aOptimum checkpoints for programs with loops 0 v97 3 aCheckpoints are widely used to improve the performance of computer systems and programs inthe presence of failures, and they significantly reduce the overall cost of running a program, if the program or the underlying system, are subject to failures. Thus application level checkpointing has been proposed for programs which may execute on platforms which are prone to failures, and also to reduce the execution time of programs which are prone to internal failures. This paper develops a mathematical model to estimate the average execution time of a program in the presence of failures, without and with application level checkpointing, and we use it to predict the optimum interval number of instructions which should be executed between the placement of successive heckpoints. The case of programs with loops and nested loops is also discussed. The results are illustrated with several numerical examples.
02334nas a2200853 4500000000100000000000100001008004100002260002100043100001900064700002100083700002300104700002100127700002400148700002500172700002500197700002800222700002700250700002000277700002100297700002500318700001800343700002500361700002100386700002200407700001800429700001300447700001800460700001900478700002100497700001600518700002100534700001800555700002100573700001900594700001900613700002200632700001900654700001900673700001700692700002100709700002300730700001600753700001800769700002300787700002500810700002200835700001900857700002400876700002400900700001900924700001800943700001800961700002000979700002400999700002001023700002201043700002301065700001501088700001701103700002001120700001801140700002401158700002101182700002101203700002301224700001401247700002401261700002001285700001801305700002401323700001901347245009601366020001801462 2018 d bRiver Publishers 1 aEnrico Ferrera 1 aClaudio Pastrone 1 aPaul-Emmanuel Brun 1 aRemi De Besombes 1 aKonstantinos Loupos 1 aGerasimos Kouloumpis 1 aPatrick O’Sullivan 1 aAlexandros Papageorgiou 1 aPanayiotis Katsoulakos 1 aBill Karakostas 1 aAntonis Mygiakis 1 aChristina Stratigaki 1 aBora Caglayan 1 aBasile Starynkevitch 1 aChristos Skoufis 1 aStelios Christofi 1 aNicolas Ferry 1 aHui Song 1 aArnor Solberg 1 aPeter Matthews 1 aAntonio Skarmeta 1 aJosé Santa 1 aMichail Beliatis 1 aMirko Presser 1 aJosiane Parreira 1 aJuan Martínez 1 aPayam Barnaghi 1 aShirin Enshaeifar 1 aThorben Iggena 1 aMarten Fischer 1 aRalf Tönjes 1 aMartin Strohbach 1 aAlessandro Sforzin 1 aHien Truong 1 aJohn Soldatos 1 aSofoklis Efremidis 1 aGeorgios Koutalieris 1 aPanagiotis Gouvas 1 aJuergen Neises 1 aGeorge Hatzivasilis 1 aIoannis Askoxylakis 1 aVivek Kulkarni 1 aArne Broering 1 aDariusz Dober 1 aKostas Ramantas 1 aChristos Verikoukis 1 aJoachim Posegga 1 aDomenico Presenza 1 aGeorge Spanoudakis 1 aDanilo Pau 1 aErol Gelenbe 1 aSławomir Nowak 1 aMateusz Nowak 1 aTadeusz Czachórski 1 aJoanna Domańska 1 aAnastasis Drosou 1 aDimitrios Tzovaras 1 aTommi Elo 1 aSanteri Paavolainen 1 aDmitrij Lagutin 1 aHelen Leligou 1 aPanagiotis Trakadas 1 aGeorge Polyzos 00 aIoT European Security and Privacy Projects: Integration, Architectures and Interoperability a9788770220071 01283nas a2200121 4500000000100000000000100001008004100002260003200043100002000075700001700095245010900112520094000221 2020 d c06/2020 bSpringer aZakopane 1 aPiotr Fröhlich 1 aErol Gelenbe 00 aOptimal fog services placement in SDN IoT network using Random Neural Networks and Cognitive Network Map 3 aDue to massive increase in number of IoT devices and number of cloud based services a crucial task arises of optimally placing (both topologically and resource-wise ) services in the network so that non of the clients will be victimized and receive best possible time of response. Also - there must be a balance not to instantiate a service on every possible machine - which would take too much resources. Task which must be solved is an optimization of parameters such as QoS between service and client, equality of clients and usage of resources. Using SDN - which is designed to answer some of the problems posed in this section such as QoS and knowledge about topology of the whole network and newly connected clients - is a gateway to better fit service management. Machine learning provides less stiff rules to follow and more intelligent behaviour of the manager.
01382nas a2200193 4500000000100000000000100001008004100002260003300043100001700076700002000093700001800113700002500131700002600156700002100182700002300203245004800226856008600274520082800360 2020 d cJune 2020 aBudva, Montenegro 1 aErol Gelenbe 1 aPiotr Fröhlich 1 aMateusz Nowak 1 aStavros Papadopoulos 1 aAikaterini Protogerou 1 aAnastasis Drosou 1 aDimitrios Tzovaras 00 aIoT Network Attack Detection and Mitigation uhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9134241&isnumber=9134063 3 aCyberattacks on the Internet of Things (IoT) can cause major economic and physical damage, and disrupt production lines, manufacturing processes, supply chains, impact the physical safety of vehicles, and damage the health of human beings. Thus we describe and evaluate a distributed and robust attack detection and mitigation system for network environments where communicating decision agents use Graph Neural Networks to provide attack alerts. We also present an attack mitigation system that uses a Reinforcement Learning driven Software Defined Network to process the alerts generated by the attack detection system, together with Quality of Service measurements, so as to re-route sensitive traffic away from compromised network paths using. Experimental results illustrate both the detection and re-routing scheme. 01121nas a2200121 4500000000100000000000100001008004100002260001800043100001700061700001600078245007900094520082600173 2020 d c05/2020 bIEEE 1 aErol Gelenbe 1 aM. Pavloski 00 aPerformance of a Security Control Scheme for a Health Data Exchange System 3 aWe propose an access control scheme to improve the security of a cross border health data access running under distributed systems such as OpenNCP that cooperate through message exchanges. The scheme allows messages that request data and services to cross different regional or national nodes after they are verified for security issues both by the sender and the receiver. It rejects those access requests that are detected to be malicious, and consequently throttles down the flow of permit messages via explicit feedback from threat detection software placed at the sender and the receiver. An analytical model is used to evaluate the message delay to determine the overhead caused by the enhanced security system, and numerical results illustrate the impact of attack rates and on the system’s performance.
00572nas a2200217 4500000000100000000000100001008004100002260001900043100002200062700002000084700001700104700002500121700001800146700002000164700002500184700002200209700002300231245006900254250001800323300001300341 2020 d bNow Publishers 1 aGianmarco Baldini 1 aPiotr Fröhlich 1 aErol Gelenbe 1 aJose Hernandez-Ramos 1 aMateusz Nowak 1 aSławomir Nowak 1 aStavros Papadopoulos 1 aAnastasios Drosou 1 aDimitrios Tzovaras 00 aIoT Network Risk Assessment and Mitigation: The SerIoT Approach aJohn Soldatos a88–104 01711nas a2200181 4500000000100000000000100001008004100002260001400043100001700057700002100074700002000095700001800115700002000133245006300153856005800216490000800274520124700282 2020 d cJuly 2020 1 aErol Gelenbe 1 aJoanna Domańska 1 aPiotr Fröhlich 1 aMateusz Nowak 1 aSławomir Nowak 00 aSelf-Aware Networks that Optimize Security, QoS and Energy uhttps://ieeexplore.ieee.org/abstract/document/9103525 0 v108 3 aThe need to adaptively manage computer systems and networks so as to offer good Quality of Service (QoS) and Quality of Experience (QoE), with secure operation at relatively low levels of energy consumption is challenged by their sheer complexity and the wide variability of the workloads. A possible way forward is through self-awareness, whereby self-measurement and self-observation, together with on-line control mechanisms which operate adaptively to attain the required performance and QoE. We survey the premises for these ideas coming from Cognitive Science and Active Networks, and review recent work on self-aware computer systems and networks, including those that propose the use of Software Defined Networks as a means to implement these concepts. Then we provide some examples from the literature on self-aware systems to illustrate the performance gains that they can provide. Finally we detail an example system and its working algorithms to allow the reader to understand how such a system may be implemented. Measurements showing how it can react rapidly to changing network conditions regarding Quality of Service (QoS) and security are also presented. Some conclusions and suggestions for further work are also presented 01134nas a2200157 4500000000100000000000100001008004100002260003700043100002000080700001700100700001800117245004100135856004900176520072900225020002200954 2020 d cJune 2020 bIEEE aDublin, Ireland 1 aPiotr Fröhlich 1 aErol Gelenbe 1 aMateusz Nowak 00 aSmart SDN Management of Fog Services uhttps://ieeexplore.ieee.org/document/9119542 3 aA Smart Service Manager is proposed to direct user requests (such as those coming from IoT devices) at the edge towards appropriate servers where the requested services can be satisfied. Services are housed at different Fog locations, and the system is subject to variations in workload. The approach is based on using a Software Defined Network (SDN) controller as the means to take decisions with measurement data based machine learning that uses Reinforcement Learning to make the best choices. The system we have developed is illustrated with experimental results on a test-bed with time-varying loads that confirm its ability to adapt to significant changes in system load and preserve the users’ Quality of Service. a978-1-7281-6728-2 00336nas a2200109 4500000000100000000000100001008004100002100001700043245014500060300001500205490000600220 2020 d 1 aErol Gelenbe 00 aIntroduction to the Special Issue on the French-Polish Collaboration in Mathematical Models of Computer Systems, Networks and Bioinformatics a44:1-44:10 0 v1 00901nas a2200157 4500000000100000000000100001000000100002008004100003260001900044100001700063700001800080245004800098250002900146520054600175022002200721 2020 d bSpringer aCham 1 aErol Gelenbe 1 aYunxiao Zhang 00 aSharing Energy for Optimal Edge Performance aChatzigeorgiou A. et al. 3 aUsing the Energy Packet Network (EPN) model, we show how energy can be shared between heterogeneous servers at the edge to minimize the overall average response time of jobs. The system is modelled as a probabilistic network where energy and jobs are being dispatched to the edge servers using G-Networks with a product-form solution for the equilibrium probability distribution of system state. The approach can also be used to design energy dispatching systems when renewable energy is used to improve the sustainability of edge computing. a978-3-030-38918-5 00700nas a2200253 4500000000100000000000100001008004100002100001600043700001800059700002100077700001700098700002300115700001700138700001900155700001800174700002300192700001700215700002200232700002300254700002200277245009400299856004600393490000700439 2019 d 1 aMarco Nalin 1 aIlaria Baroni 1 aGiuliana Faiella 1 aMaria Romano 1 aFlavia Matrisciano 1 aErol Gelenbe 1 aDavid Martinez 1 aJos Dumortier 1 aPantelis Natsiavas 1 aKostas Votis 1 aVassilis Koutkias 1 aDimitrios Tzovaras 1 aFabrizio Clemente 00 aThe European cross-border health data exchange roadmap: Case study in the Italian setting uhttps://doi.org/10.1016/j.jbi.2019.103183 0 v94 01082nas a2200409 4500000000100000000000100001008004100002100001900043700001900062700002000081700002200101700001900123700002100142700001700163700001800180700001700198700001600215700001500231700002000246700002100266700002500287700002400312700002000336700001800356700001700374700002500391700001400416700001600430700001200446700002200458700001800480700001700498245009500515856003600610300001900646490000700665 2019 d 1 aRajkumar Buyya 1 aSatish Srirama 1 aGiuliano Casale 1 aRodrigo Calheiros 1 aYogesh Simmhan 1 aBlesson Varghese 1 aErol Gelenbe 1 aBahman Javadi 1 aLuis Vaquero 1 aMarco Netto 1 aAdel Toosi 1 aMaria Rodriguez 1 aIgnacio Llorente 1 aSabrina di Vimercati 1 aPierangela Samarati 1 aDejan Milojicic 1 aCarlos Varela 1 aRami Bahsoon 1 aMarcos de Assunção 1 aOmer Rana 1 aWanlei Zhou 1 aHai Jin 1 aWolfgang Gentzsch 1 aAlbert Zomaya 1 aHaiying Shen 00 aA Manifesto for Future Generation Cloud Computing: Research Directions for the Next Decade uhttps://doi.org/10.1145/3241737 a105:1–105:38 0 v51 00353nas a2200133 4500000000100000000000100001008004100002100001900043700001700062245007200079856004700151300001400198490000700212 2019 d 1 aYasin Kadioglu 1 aErol Gelenbe 00 aProduct-Form Solution for Cascade Networks With Intermittent Energy uhttps://doi.org/10.1109/JSYST.2018.2854838 a918–927 0 v13 00494nas a2200181 4500000000100000000000100001008004100002100001100043700001700054700001900071700001700090700001300107700002000120245010300140856004600243300001600289490000700305 2019 d 1 aJun Du 1 aErol Gelenbe 1 aChunxiao Jiang 1 aHaijun Zhang 1 aYong Ren 1 aH. Vincent Poor 00 aPeer Prediction-Based Trustworthiness Evaluation and Trustworthy Service Rating in Social Networks uhttps://doi.org/10.1109/TIFS.2018.2883000 a1582–1594 0 v14 00476nas a2200181 4500000000100000000000100001008004100002100001100043700001900054700001700073700001700090700001300107700001400120245009200134856004500226300001600271490000700287 2019 d 1 aJun Du 1 aChunxiao Jiang 1 aErol Gelenbe 1 aHaijun Zhang 1 aYong Ren 1 aTony Quek 00 aDouble Auction Mechanism Design for Video Caching in Heterogeneous Ultra-Dense Networks uhttps://doi.org/10.1109/TWC.2019.2895076 a1669–1683 0 v18 00666nas a2200229 4500000000100000000000100001008004100002260000900043100002800052700002200080700001900102700002300121700002400144700003000168700002300198700002300221700001300244700001700257700001700274245009800291856004700389 2019 d bIEEE 1 aJavier Augusto-Gonzalez 1 aAnastasija Collen 1 aS. Evangelatos 1 aM. Anagnostopoulos 1 aGeorgios Spathoulas 1 aKonstantinos Giannoutakis 1 aKonstantinos Votis 1 aDimitrios Tzovaras 1 aB. Genge 1 aErol Gelenbe 1 aNiels Nijdam 00 aFrom Internet of Threats to Internet of Things: A Cyber Security Architecture for Smart Homes uhttps://doi.org/10.1109/CAMAD.2019.8858493 00351nas a2200133 4500000000100000000000100001008004100002260000900043100002200052700001700074700001600091245005500107856005500162 2019 d bIEEE 1 aMiltiadis Siavvas 1 aErol Gelenbe 1 aMeikang Qiu 00 aOptimum Interval for Application-level Checkpoints uhttps://doi.org/10.1109/CSCloud/EdgeCom.2019.000-4 00468nas a2200169 4500000000100000000000100001008004100002260000900043100001700052700002200069700002000091700002100111700001800132700001700150245007900167856005200246 2019 d bIEEE 1 aErol Gelenbe 1 aFloriano De Rango 1 aCarlos Calafate 1 aMiroslav Voznák 1 aAlfredo Garro 1 aMauro Tropea 00 aEnergy Consumption and Quality of Service in Computer Systems and Networks uhttps://doi.org/10.1109/DS-RT47707.2019.8958673 00967nas a2200361 4500000000100000000000100001008004100002260000900043100002700052700001600079700001800095700002200113700002100135700002300156700002100179700002000200700001700220700001500237700002400252700002400276700001700300700001800317700001800335700001700353700002000370700002200390700001700412700001400429700002100443700001900464245007800483856004400561 2019 d bIEEE 1 aSotiris Diamantopoulos 1 aMarco Nalin 1 aIlaria Baroni 1 aFabrizio Clemente 1 aGiuliana Faiella 1 aCharis Mesaritakis 1 aEvangelos Grivas 1 aJanne Rasmussen 1 aJan Petersen 1 aIsaac Cano 1 aElisa Puigdomènech 1 aDimitris Karamitros 1 aErol Gelenbe 1 aJos Dumortier 1 aMaja Voronkov 1 aLuigi Romano 1 aLuigi Coppolino 1 aVassilis Koutkias 1 aKostas Votis 1 aOana Stan 1 aPaolo Campegiani 1 aDavid Martinez 00 aSecure Cross-Border Exchange of Health Related Data: The KONFIDO Approach uhttps://doi.org/10.1109/EDCC.2019.00024 00513nas a2200169 4500000000100000000000100001008004100002260000900043100002500052700002400077700002200101700001700123700001600140700001800156245011700174856005200291 2019 d bIEEE 1 aAlexander Frötscher 1 aBernhard Monschiebl 1 aAnastasios Drosou 1 aErol Gelenbe 1 aMartin Reed 1 aMays Al-Naday 00 aImprove cybersecurity of C-ITS Road Side Infrastructure Installations: the SerIoT - Secure and Safe IoT approach uhttps://doi.org/10.1109/ICCVE45908.2019.8965056 01145nas a2200421 4500000000100000000000100001008004100002260001300043100002700056700002400083700001700107700002000124700002200144700001700166700001400183700002100197700001900218700001600237700001800253700002200271700002100293700002300314700002100337700002000358700001700378700001500395700002400410700001700434700001800451700002000469700002200489700002200511700002200533700002200555700001900577245007800596856004900674 2019 d bSpringer 1 aSotiris Diamantopoulos 1 aDimitris Karamitros 1 aLuigi Romano 1 aLuigi Coppolino 1 aVassilis Koutkias 1 aKostas Votis 1 aOana Stan 1 aPaolo Campegiani 1 aDavid Martinez 1 aMarco Nalin 1 aIlaria Baroni 1 aFabrizio Clemente 1 aGiuliana Faiella 1 aCharis Mesaritakis 1 aEvangelos Grivas 1 aJanne Rasmussen 1 aJan Petersen 1 aIsaac Cano 1 aElisa Puigdomènech 1 aErol Gelenbe 1 aJos Dumortier 1 aMaja Voss-Knude 1 aRaffaele Montella 1 aAngelo Ciaramella 1 aGiancarlo Fortino 1 aAntonio Guerrieri 1 aAntonio Liotta 00 aSecure Cross-Border Exchange of Health Related Data: The KONFIDO Approach uhttps://doi.org/10.1007/978-3-030-34914-1_30 00406nas a2200133 4500000000100000000000100001008004100002100001900043700002200062700001700084245011600101856003600217490001900253 2019 d 1 aKhaled Hussain 1 aMohamed Bassyouni 1 aErol Gelenbe 00 aAccurate and Energy-Efficient Classification with Spiking Random Neural Network: Corrected and Expanded Version uhttp://arxiv.org/abs/1906.08864 0 vabs/1906.08864 00322nas a2200121 4500000000100000000000100001008004100002100001300043700001700056245007200073856003600145490001900181 2019 d 1 aHuibo Bi 1 aErol Gelenbe 00 aEmergency Management Systems and Algorithms: a Comprehensive Survey uhttp://arxiv.org/abs/1907.04136 0 vabs/1907.04136 00959nas a2200349 4500000000100000000000100001008004100002100002300043700002000066700002000086700002300106700002000129700002100149700001500170700001600185700002100201700002200222700001600244700002100260700001400281700001700295700001800312700001700330700002300347700001700370700002000387700002200407245011000429856004600539300001700585490000700602 2018 d 1 aPantelis Natsiavas 1 aJanne Rasmussen 1 aMaja Voss-Knude 1 aKonstantinos Votis 1 aLuigi Coppolino 1 aPaolo Campegiani 1 aIsaac Cano 1 aDavid Marí 1 aGiuliana Faiella 1 aFabrizio Clemente 1 aMarco Nalin 1 aEvangelos Grivas 1 aOana Stan 1 aErol Gelenbe 1 aJos Dumortier 1 aJan Petersen 1 aDimitrios Tzovaras 1 aLuigi Romano 1 aIoannis Komnios 1 aVassilis Koutkias 00 aComprehensive user requirements engineering methodology for secure and interoperable health data exchange uhttps://doi.org/10.1186/s12911-018-0664-0 a85:1–85:16 0 v18 00437nas a2200181 4500000000100000000000100001008004100002100001100043700001900054700001700073700001100090700001500101700001300116245006200129856004500191300001200236490000700248 2018 d 1 aJun Du 1 aChunxiao Jiang 1 aErol Gelenbe 1 aLei Xu 1 aJianhua Li 1 aYong Ren 00 aDistributed Data Privacy Preservation in IoT Applications uhttps://doi.org/10.1109/MWC.2017.1800094 a68–76 0 v25 00977nas a2200349 4500000000100000000000100001008004100002260001300043100002200056700001700078700002800095700002200123700003000145700002400175700001700199700002300216700002300239700001500262700002100277700001900298700001600317700001300333700001700346700002100363700002400384700002200408700002000430700001700450700002300467245008900490856004800579 2018 d bSpringer 1 aAnastasija Collen 1 aNiels Nijdam 1 aJavier Augusto-Gonzalez 1 aSokratis Katsikas 1 aKonstantinos Giannoutakis 1 aGeorgios Spathoulas 1 aErol Gelenbe 1 aKonstantinos Votis 1 aDimitrios Tzovaras 1 aN. Ghavami 1 aMelanie Volkamer 1 aPiroska Haller 1 aA. Sánchez 1 aM. Dimas 1 aErol Gelenbe 1 aPaolo Campegiani 1 aTadeusz Czachórski 1 aSokratis Katsikas 1 aIoannis Komnios 1 aLuigi Romano 1 aDimitrios Tzovaras 00 aGHOST - Safe-Guarding Home IoT Environments with Personalised Real-Time Risk Control uhttps://doi.org/10.1007/978-3-319-95189-8_7 00540nas a2200205 4500000000100000000000100001008004100002260001300043100001800056700001600074700002000090700001700110700001800127700002400145700001900169700002000188700002300208245005400231856004900285 2018 d bSpringer 1 aIngrid Grenet 1 aYonghua Yin 1 aJean-Paul Comet 1 aErol Gelenbe 1 aVera Kurková 1 aYannis Manolopoulos 1 aBarbara Hammer 1 aLazaros Iliadis 1 aIlias Maglogiannis 00 aMachine Learning to Predict Toxicity of Compounds uhttps://doi.org/10.1007/978-3-030-01418-6_33 00331nas a2200121 4500000000100000000000100001008004100002260000900043100001700052700001900069245007500088856004600163 2018 d bIEEE 1 aErol Gelenbe 1 aYasin Kadioglu 00 aEnergy Life-Time of Wireless Nodes with Network Attacks and Mitigation uhttps://doi.org/10.1109/ICCW.2018.8403561 00480nas a2200169 4500000000100000000000100001008004100002260001300043100002200056700001700078700002400095700001700119700002200136700001700158245008600175856004900261 2018 d bSpringer 1 aOlumide Akinwande 1 aErol Gelenbe 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aKrzysztof Grochla 1 aRicardo Lent 00 aA Reinforcement Learning Approach to Adaptive Forwarding in Named Data Networking uhttps://doi.org/10.1007/978-3-030-00840-6_23 00453nas a2200169 4500000000100000000000100001008004100002260000900043100001100052700001900063700001700082700001200099700001300111700001900124245009300143856004700236 2018 d bIEEE 1 aJun Du 1 aChunxiao Jiang 1 aErol Gelenbe 1 aZhu Han 1 aYong Ren 1 aMohsen Guizani 00 aNetworked Data Transaction in Mobile Networks: A Prediction-based Approach Using Auction uhttps://doi.org/10.1109/IWCMC.2018.8450531 00444nas a2200169 4500000000100000000000100001008004100002260000900043100001100052700001700063700001900080700001200099700001300111700001900124245008400143856004700227 2018 d bIEEE 1 aJun Du 1 aErol Gelenbe 1 aChunxiao Jiang 1 aZhu Han 1 aYong Ren 1 aMohsen Guizani 00 aCognitive Data Allocation for Auction-based Data Transaction in Mobile Networks uhttps://doi.org/10.1109/IWCMC.2018.8450428 00662nas a2200205 4500000000100000000000100001008004100002260001300043100001700056700002100073700002400094700002200118700002000140700001700160700002300177245018000200856004600380490000800426020002200434 2018 d bSpringer 1 aErol Gelenbe 1 aPaolo Campegiani 1 aTadeusz Czachórski 1 aSokratis Katsikas 1 aIoannis Komnios 1 aLuigi Romano 1 aDimitrios Tzovaras 00 aSecurity in Computer and Information Sciences - First International ISCIS Security Workshop 2018, Euro-CYBERSEC 2018, London, UK, February 26-27, 2018, Revised Selected Papers uhttps://doi.org/10.1007/978-3-319-95189-8 0 v821 a978-3-319-95188-1 00514nas a2200157 4500000000100000000000100001008004100002260001300043100002400056700001700080700002200097700001700119245019000136490000800326020002200334 2018 d bSpringer 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aKrzysztof Grochla 1 aRicardo Lent 00 aComputer and Information Sciences - 32nd International Symposium, ISCIS 2018, Held at the 24th IFIP World Computer Congress, WCC 2018, Poznan, Poland, September 20-21, 2018, Proceedings 0 v935 a978-3-030-00839-0 01382nas a2200145 4500000000100000000000100001008004100002260002400043100002400067700001700091700001900108700001800127245004400145520104700189 2020 d bIEEE aMilano, Italy 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aGodlove Kuaban 1 aDariusz Marek 00 aTransient Behaviour of a Network Router 3 aThe broader use of Software Defined Network (SDN) controllers creates periodic changes in topology and traffic rates at routers that adapt the network to changes in network conditions.
Thus the transient behaviour of network components, and in particular routers, is becoming of great interest.
Since standard queueing models are difficult to analyze under time-varying conditions, we propose a tractable diffusion approximation for both the transient and steady-state behaviour of a network router. In particular, the analysis provides the steady-state and transient delay and packet loss probability as a function of traffic load and other characteristics. Using these results, we show that when SDN routers change the paths of flows frequently, the network's behaviour may often be far from its steady-state behaviour. Therefore any network optimization conducted with the help of SDN should not be based on steady-state behaviour, but rather on some metric reated to the network time dependent behaviour.
The frequency, destruction and costs of natural and human-made disasters in modern
highly-populated societies have resulted in research on emergency evacuation and wayfinding, which
has drawn considerable attention. The subject is now a multidisciplinary area of research where
information and communication technologies (ICT), and in particular the Internet of Things (IoT),
have a significant impact on sensing and computing dynamic reactions that mitigate or prevent
the worst outcomes of disasters. This paper offers state-of-the-art knowledge in this area so as to
share ongoing research results, identify the research gaps and address the need for future research.
We present a comprehensive review of research on emergency evacuation and wayfinding, focusing
on the algorithmic and system design aspects. Starting from the history of emergency management
research, we identify the emerging challenges concerning system optimisation, evacuee behaviour
optimisation and data analysis, and the additional energy consumption by ICT equipment that
operates the emergency management infrastructure.
This paper proposes a Neurocomputing application that reorders the Web results obtained from different Web Search Engines emulating the way our brain takes decisions. The proposed application is based on the Random Neural Network with Deep Learning Clusters that evaluates and adapts Web result relevance by associating independently each Deep Learning Cluster to a specific Web Search Engine. In addition, this paper presents a Deep Learning Cluster to perform as a Management Cluster that decides the final result relevance based on the inputs from each independent Deep Learning cluster. The performance of the proposed Management Cluster is evaluated when included as an additional layer to the Deep Learning Clusters. On average; the proposed Deep Learning cluster structure improves Smart Search performance
a0925-2312 01554nas a2200193 4500000000100000000000100001008004100002260005600043100002400099700001700123700001900140700001800159245008100177250003000258856003300288300001000321490000900331520102000340 2020 d c12/2020 bCEUR-WS.org aPetrozavodsk, Karelia, Russia 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aGodlove Kuaban 1 aDariusz Marek 00 aTime Dependent Diffusion Model for Security Driven Software Defined Networks aCEUR Workshop Proceedings uhttp://ceur-ws.org/Vol-2792/ a38-56 0 v2792 3 aWe present a model of a Software Defined Network (SDN) where frequent changes in routing and traffic rates at routers are needed to respond to the security, quality of service (QoS), and energy savings requirements of applications such as the Internet of Things.
Such frequent path and traffic changes introduce time-dependent network behaviours, and standard queueing models are not well adapted to analyse the transient regime, we propose a tractable diffusion approximation for both the transient and steady-state behaviour.
Our model can represent any network topology transmitting time-dependent flows with routing changes, and computes queue length and delay distributions at each network node and along complete paths between senders and receivers.
Using realistic router parameters, we show that transients occupy a significant fraction of system time, so that the optimisation conducted with SDN controllers needs to include the effect of time-dependent behaviours.
The Cognitive Packet Network (CPN) bases its routing decisions and flow control on the Random Neural Network (RNN) Reinforcement Learning algorithm; this paper proposes the addition of a Deep Learning (DL) Cluster management structure to the CPN for Quality of Service metrics (Delay Loss and Bandwidth), Cyber Security keys (User, Packet and Node) and Management decisions (QoS, Cyber and CEO). The RNN already models how neurons transmit information using positive and negative impulsive signals whereas the proposed additional Deep Learning structure emulates the way the brain learns and takes decisions; this paper presents a brain model as the combination of both learning algorithms, RNN and DL. The proposed model has been simulated under different network sizes and scenarios and it has been validated against the CPN itself without DL clusters. The simulation results are promising; the presented CPN with DL clusters as a mechanism to transmit, learn and make packet routing decisions is a step closer to emulate the way the brain transmits information, learns the environment and takes decisions.
a0925-2312 01780nas a2200181 4500000000100000000000100001008004100002260001200043100001100055700001700066700001900083700001200102700001300114245010300127490000700230520134700237022001401584 2020 d c05/2020 1 aJun Du 1 aErol Gelenbe 1 aChunxiao Jiang 1 aZhu Han 1 aYong Ren 00 aAuction-Based Data Transaction in Mobile Networks: Data Allocation Design and Performance Analysis 0 v19 3 aMobile data traffic is experiencing unprecedented increases due to the proliferation of highly capable smartphones, laptops and tablets, and mobile data offloading can be used to move traffic from cellular networks to other wireless infrastructures such as small-cell base stations. This work addresses the related issue of data allocation, by proposing a novel infrastructure independent method based on the hotspot function of smartphones. In the proposed scheme, smartphones transfer data allowances among mobile users, so that users with excess data allowances act as accessible Wi-Fi hotspots, selling their data allowance to other users who need extra data allowances. To achieve this objective, we propose to use auctions with single and multiple data sellers. Efficient schemes based on auction models are discussed to sell the data allowances over successive days in a month, and over different time slots during a single day. Overall system performance is considered based on the behavior of mobile users, such as changing demands for the sale or purchase of data allowances. Together with the analytical results presented, our simulation experiments also indicate that knowledge of user behavior can significantly improve the performance of data allowance transactions, leading to highly efficient allocations among users.
a1558-0660 01417nas a2200181 4500000000100000000000100001008004100002260005600043100002400099700001700123700001800140245005300158250006500211856005100276490001000327520087600337020002201213 2020 d c12/2020 bSpringer International Publishing aon-line 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aDariusz Marek 00 aSoftware Defined Network Dynamics via Diffusions aComputer Communication Networks and Telecommunications, LNCS uhttps://www.springer.com/gp/book/9783030681098 0 v12527 3 aSoftware-Defined Networks (SDN) dynamically modify the paths of Internet flows in response to the quality of service or security needs, and hence frequently modify traffic levels at network routers. Thus network routers often operate in the transient regime, rather than at steady-state, with significant impact on packet loss probabilities and delay. We, therefore, investigate the time-dependent performance of a small network of routers, modelled as G/G/1/N queueing stations. A diffusion approximation is developed to predict the quality of service of the routers in the transient regime. Numerical examples show that the results in the transient regime can differ very significantly from the steady-state results, and therefore that the transient analysis must be taken into account in evaluating the performance of routers in a SDN network.
a978-3-030-68110-4 00540nas a2200181 4500000000100000000000100001008004100002260003800043100002000081700002200101700002100123700001700144245008700161856006600248300001200314490001000326022002200336 2021 d bSpringer International Publishing 1 aKatarzyna Filus 1 aMiltiadis Siavvas 1 aJoanna Domańska 1 aErol Gelenbe 00 aThe Random Neural Network as a Bonding Model for Software Vulnerability Prediction uhttps://link.springer.com/chapter/10.1007/978-3-030-68110-4_7 a102-116 0 v12527 a978-3-030-68110-4 01179nas a2200181 4500000000100000000000100001008004100002260003800043100002000081700002100101700001700122245007000139856006600209300001000275490001000285520068000295022002200975 2021 d bSpringer International Publishing 1 aKatarzyna Filus 1 aJoanna Domańska 1 aErol Gelenbe 00 aRandom Neural Network for Lightweight Attack Detection in the IoT uhttps://link.springer.com/chapter/10.1007/978-3-030-68110-4_5 a79-91 0 v12527 3 aCyber-attack detection has become a basic component of all information processing systems, and once an attack is detected it may be possible to block or mitigate its effects. This paper addresses the use of a learning recurrent Random Neural Network (RNN) to build a lightweight detector for certain types of Botnet attacks on IoT systems. Its low computational cost based on a small 12-neuron recurrent architecture makes it particularly attractive for edge devices. The RNN can be trained off-line using a fast simplified gradient descent algorithm, and we show that it can lead to high detection rates of the order of 96%, with false alarm rates of a few percent.
a978-3-030-68110-4 00460nas a2200169 4500000000100000000000100001008004100002100002000043700002000063700002100083700002200104700001700126245007700143856004500220490001100265022001400276 2021 d 1 aKatarzyna Filus 1 aPaweł Boryszko 1 aJoanna Domańska 1 aMiltiadis Siavvas 1 aErol Gelenbe 00 aEfficient Feature Selection for Static Analysis Vulnerability Prediction uhttps://www.mdpi.com/1424-8220/21/4/1133 0 v21 (4) a1424-8220 01378nas a2200157 4500000000100000000000100001008004100002100002400043700001700067700001900084700001800103245008100121300001400202490000900216520099500225 2020 d 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aGodlove Kuaban 1 aDariusz Marek 00 aTime Dependent Diffusion Model for Security Driven Software Defined Networks app. 38-56 0 v2792 3 aWe present a model of a Software Defined Network (SDN) where frequent changes in routing and traffic rates at routers are needed
to respond to the security, quality of service (QoS), and energy savings requirements of applications such as the Internet of Things. Such frequent path and traffic changes introduce time-dependent network behaviours, and standard queueing models are not well adapted to analyse the transient regime, we propose a tractable diffusion approximation for both the transient and steady-state behaviour. Our model can represent any network topology transmitting time-dependent flows with routing changes, and computes queue length and delay distributions at each network node and along complete paths between senders and receivers. Using realistic router parameters, we show that transients occupy a significant fraction of system time, so that the optimisation conducted with SDN controllers needs to include the effect of time-dependent behaviours.
It has been recently observed that Software Defined Networks (SDN) can change the
paths of different connections in the network at a relatively frequent pace to improve the overall
network performance, including delay and packet loss, or to respond to other needs such as security.
These changes mean that a network that SDN controls will seldom operate in steady state; rather,
the network may often be in transient mode, especially when the network is heavily loaded and
path changes are critically important. Hence, we propose a transient analysis of such networks to
better understand how frequent changes in paths and the switches’ workloads may affect multi-hop
networks’ performance. Since conventional queueing models are difficult to solve for transient
behaviour and simulations take excessive computation time due to the need for statistical accuracy,
we use a diffusion approximation to study a multi-hop network controlled by SDN. The results show
that network optimization should consider the transient effects of SDN and that transients need to be
included in the design of algorithms for SDN controllers that optimize network performance.
The Conference on Energy Consumption, Quality of Service, Reliability, Security, and Maintainability of Computer Systems and Networks (EQSEM) was held as a virtual conference on October 20–21, 2020. This paper summarises the objectives and proceedings of this conference. It then briefly presents the keynotes and other papers which were presented. Then, in the context of the EU-funded research project SDK4ED which motivated this conference, we outline several solutions that are being developed for managing the potential inter-dependencies and corresponding trade-offs between design-time and runtime qualities in software applications, and review the key functionalities that have been implemented within the SDK4ED integrated platform as of this writing. Then, we briefly introduce four papers among those presented at the EQSEM Conference that are included in this issue of the journal SN Computer Science, presenting relevant research achievements of the SDK4ED Project on software maintainability, reliability, and energy efficiency.
01829nas a2200121 4500000000100000000000100001008004100002100001700043700002200060245006300082490000700145520155500152 2021 d 1 aErol Gelenbe 1 aMiltiadis Siavvas 00 aMinimizing Energy and Computation in Long-Running Software 0 v11 3 aLong-running software may operate on hardware platforms with limited energy resources
such as batteries or photovoltaic, or on high-performance platforms that consume a large amount
of energy. Since such systems may be subject to hardware failures, checkpointing is often used to
assure the reliability of the application. Since checkpointing introduces additional computation time
and energy consumption, we study how checkpoint intervals need to be selected so as to minimize a
cost function that includes the execution time and the energy. Expressions for both the program’s
energy consumption and execution time are derived as a function of the failure probability per
instruction. A first principle based analysis yields the checkpoint interval that minimizes a linear
combination of the average energy consumption and execution time of the program, in terms of the
classical “Lambert function”. The sensitivity of the checkpoint to the importance attributed to energy
consumption is also derived. The results are illustrated with numerical examples regarding programs
of various lengths and showing the relation between the checkpoint interval that minimizes energy
consumption and execution time, and the one that minimizes a weighted sum of the two. In addition,
our results are applied to a popular software benchmark, and posted on a publicly accessible web
site, together with the optimization software that we have developed.
Input traffic from Internet of Things (IoT) devices is often both periodic and requires to be received by a given deadline. This can create congestion at instants of time when traffic flowing from multiple devices arrives at a shared input port or gateway, resulting in missed deadlines at the receiver. As a consequence, scheduling techniques such as the “Earliest Deadline First” (EDF) and “Priority based on Average Load” (PAL) are used to schedule the flow from different devices so as to try to satisfy the needs of the largest number of traffic flows in a timely fashion. In this paper, we propose the Randomization of flow Genaration Times (RGT) in order to smooth the total incoming traffic to the input port or gateway, on top of the use of EDF and PAL. We then evaluate the performance of RGT together with PAL and EDP, for traffic load with a varying number of up to 6400 IoT devices. Our simulation results show that RGT provides significantly better performance when added to EDF and PAL. Also, the additional computation required by RGT at each device can be quite small, suggesting that RGT is a very useful addition for improving the performance of IoT networks.
00989nas a2200109 4500000000100000000000100001008004100002100001700043245007700060490000600137520073600143 2021 d 1 aErol Gelenbe 00 aRenforcer la place des universités européennes dans le concert mondial 0 v2 3 aLes pays de l’UE peuvent se targuer de posséder quelques-unes des plus prestigieuses universités et organismes de recherche dans le monde. Pourtant la part des innovations technologiques que produit le continent reste faible au regard du potentiel de recherches que l’UE représente. Comment redresser la barre et mieux armer les universités européennes dans la compétition exacerbée que se livrent les grandes puissances ? Après une analyse du financement et du mode de fonctionnement comparés des universités dans le monde occidental, l’auteur propose quelques recommandations fortes pour donner à la France et l’Europe de la recherche les moyens de se confronter à armes égales avec ses concurrents.
01229nas a2200145 4500000000100000000000100001008004100002260000900043100001100052700001400063700001500077700001700092245009100109520088300200 2021 d bIEEE 1 aNan Li 1 aXiping Hu 1 aEdith Ngai 1 aErol Gelenbe 00 aCooperative Wireless Edges with Composite Resource Allocation in Hierarchical Networks 3 aWith the expansion of the IoT, it is important to optimize available bandwidth to reliably support edge to device communications. Thus we propose a wireless network where each edge server communicates with its end devices using its wireless band as a primary channel, assisted by a secondary edge server that can relay communications via its own wireless band as a secondary channel. The network can optimize capacity by balancing load between primary and secondary wireless bands, and we analyze the geometry of achievable rate regions, depending on the state of bands modeled as Rayleigh fading channels. The allocation of a connection to the primary or secondary band is formulated as an optimization problem which is then solved, and illustrated with numerical examples. Index Terms—Edge computing, cooperation, non-orthogonal multiple access, resource allocation
01621nas a2200193 4500000000100000000000100001008004100002260001500043100002000058700001700078700001800095700002200113700001800135700002000153245006800173856004500241490001000286520113100296 2021 d cApril 2021 1 aPiotr Fröhlich 1 aErol Gelenbe 1 aJerzy Fiołka 1 aJacek Chęciński 1 aMateusz Nowak 1 aZdzisław Filus 00 aSmart SDN Management of Fog Services to Optimize QoS and Energy uhttps://www.mdpi.com/1424-8220/21/9/3105 0 v21(9) 3 aThe short latency required by IoT devices that need to access specific services have led to the development of Fog architectures that can serve as a useful intermediary between IoT systems and the Cloud. However, the massive numbers of IoT devices that are being deployed raise concerns about the power consumption of such systems as the number of IoT devices and Fog servers increase. Thus, in this paper, we describe a software-defined network (SDN)-based control scheme for client–server interaction that constantly measures ongoing client–server response times and estimates network power consumption, in order to select connection paths that minimize a composite goal function, including both QoS and power consumption. The approach using reinforcement learning with neural networks has been implemented in a test-bed and is detailed in this paper. Experiments are presented that show the effectiveness of our proposed system in the presence of a time-varying workload of client-to-service requests, resulting in a reduction of power consumption of approximately 15% for an average response time increase of under 2%. 02819nas a2200253 4500000000100000000000100001008004100002260004300043100001800086700002100104700001900125700001600144700002100160700001700181700001900198700002100217700002400238700002500262700001800287700001600305245005000321300001200371520218200383 2021 d bALLEA - All European Academies aBerlin 1 aDan Larhammar 1 aMaria Baghramian 1 aPiero Bianucci 1 aGarvin Brod 1 aDalibor Dobiáš 1 aErol Gelenbe 1 aRisto Kunelius 1 aStefano Leonardi 1 aStephan Lewandowsky 1 aMariëtte Samenweten 1 aAlison Powell 1 aJane Suiter 00 aFact or Fake? Tackling Science Disinformation apaper 5 3 aThe information landscape has undergone dramatic changes with the expansion of the internet and digital social media platforms. Information can be spread more rapidly and can reach more people than ever before. While this offers excellent possibilities to teach and educate and to disseminate information about research results and scientific evidence, it also comes with a downside: False information can be propagated with equal ease and speed. This discussion paper describes and discusses the problems and the consequences of science disinformation in three areas of concern, namely climate change, vaccines and pandemics, and what we can do to increase awareness and minimize harm caused by the spread of disinformation. It does so by highlighting the societal value of the scientific method, research integrity, open science communication and the resulting trust in science. The underlying question is how to protect the pillars of science from the severe consequences of disinformation while maintaining openness and democratic principles. This paper presents the central characteristics of science disinformation, its roots, its spread, and potential solutions. The mere existence of disinformation is hard to prevent in open societies with strong protection of individual rights and freedom of expression. The paper identifies underlying cognitive, social and economic mechanisms that amplify the spread of disinformation and evaluates potential solutions. Extensive research over the past several years has identified cognitive features of the human mind, as well as fast and efficient transmission channels, that contribute to the prevalence of science disinformation in our societies. Potential solutions cover a range of psychological, technical and political measures including inoculation, debunking, recommender systems, fact-checking, raising awareness, media literacy, and innovations in science communication and public engagement. Together, they contribute to tackling problems such as knowledge resistance, pseudoscience, undermining of trust, confirmation bias, filter bubbles, echo chambers, and other problems related to science disinformation.
04152nam a2200169 4500000000100000000000100001008004100002260001300043100001600056700001700072700002500089700002000114700001800134245005300152490000600205520377100211 2020 d bSpringer 1 aSabu Thampi 1 aErol Gelenbe 1 aMohammed Atiquzzaman 1 aVipin Chaudhary 1 aKuan-Ching Li 00 aAdvances in Computing and Network Communications 0 v1 3 aThe Fourth International Conference on Computing and Network Communications (CoCoNet’20) provided a forum for sharing original research outcomes and practical development experiences among experts in the emerging areas of computing and communications. The conference was organized by Vellore Institute of Technology (VIT), Chennai, India. Due to the recent pandemic situation, the conference was conducted as a virtual event during October 14–17, 2020. The material was presented in a program that consisted of keynote talks, technical sessions, lightning talks, tutorials, symposiums, parallel sessions, workshops and hot off the press. CoCoNet’20 convened a well-tailored and handpicked collection of eminent speakers from renowned universities and industries in and outside India. The array of speakers included Dr. Ian T. Foster, Dr. Arumugam Nallanathan, Dr. Mohammed Atiquzzaman, Dr. Vipin Chaudhary, Dr. Dilip Krishnaswamy and Dr. Ljiljana Trajkovic. The conference received 181 submissions this year, out of which 98 papers (58 regular papers and 40 short papers) had been accepted. The papers were subjected to a rigorous review process that examined the significance, novelty and technical quality of the submission. The papers were presented in different sessions, namely the best paper sessions, regular paper sessions and short paper sessions. The proceedings of the conference is organized into two volumes. This volume is comprised of 51 papers, and the topical sections include Communications, Control and Signal Processing, Data Analytics and Networked Systems and Security. The success of the conference depends ultimately on the numerous people who have worked with us to plan and organize both the technical program and local arrangements. In particular, the wise advice and brilliant ideas of the program chairs, workshop and symposium chairs and industry track chairs in the organization of the technical program are gratefully appreciated. We would like to extend our heartfelt gratitude to the organizing committee and advisory committee members. The accomplishment of the motives of the conference is powered by the tireless efforts of many individuals. We would like to express our sincere gratitude to the TPC chairs, TPC members and additional reviewers who shared their technical expertise and assisted us in reviewing all the submitted papers. We would like to thank the general chairs, organizing committee members, steering committee, keynote speakers, session chairs and the conference attendees. We are thankful to all the authors for choosing this conference as a venue for presenting their research works. We express our wholehearted appreciation to the contributions of all those who apportioned their valuable time for the success of CoCoNet’20. We are grateful to Vellore Institute of Technology (VIT), Chennai, for organizing the conference. Recognition should go to the local organizing committee members who all have worked extremely hard for the details of important aspects of the conference programs. We appreciate the contributions of all the faculty and staff of VIT and the student volunteers who have selflessly contributed their time to make this virtual conference successful. We would like to express our gratitude to Senior Editor of Springer Nature, Aninda Bose, for his help and cooperation. We sincerely hope that CoCoNet’20 turned out to be a forum for excellent discussions that enabled new ideas to come about, promoting collaborative research. We are confident that the proceedings will serve as a momentous source of research references and knowledge, which will lead not only to the scientific and engineering findings but also to the development of new products and technologies.
02264nas a2200205 4500000000100000000000100001008004100002100001700043700001700060700001700077700002200094700002300116700002000139700002200159700002000181700002000201245005900221490000900280520176900289 2021 d 1 aErol Gelenbe 1 aGuy Brasseur 1 aLuc Chefneux 1 aVéronique Devant 1 aVéronique Halloin 1 aJean-Paul Haton 1 aMichel Judkiewicz 1 aBernard Rentier 1 aRomain Weikmans 00 aOn Sharing Knowledge And Fostering “Open Science” 0 v2021 3 aThe crucial importance of science and technology and its accurate peer reviewed dissemination, has once again been demonstrated during the current pandemic. Thus the COVID-19 pandemic together with the inevitable energy transition required by climate change, lead us to consider the issue of scientific and technical communication, both for the written papers and proceedings that have largely moved online (but not always in open access), and the various types of seminars, workshops, and symposia that frequently involve air travel with substantial CO2 impact. Online meetings that have become recently very popular, as well as online repositories for publications, themselves have a significant CO2—as well as environmental—impact, due to the massive use of electricity by information and communication technologies (ICT) and of the environmentally unfriendly manufacturing processes and decommissioning of ICT equipment. Presented is a broad overview of these aspects, and some recommendations regarding the future organization of scientific and technical communication, including: (1) peer-reviewed journals and proceedings with online open access; (2) the importance of face to face seminars and symposia, together with online meetings, to maintain the serendipity and importance of direct human contact while reducing the need for air travel; (3) the peer evaluation of research and of academic and research staff and its dependence on publications and their qualitative—rather than excessively quantitative—evaluation, where the concept of impact should include the usefulness of research to education, industry and society; (4) and the crucial role of ICT in all these aspects and the questions raised by the sustainability of ICT itself.
02365nas a2200145 4500000000100000000000100001008004100002100002200043700002300065700002300088700001700111245011500128490000700243520196900250 2021 d 1 aMiltiadis Siavvas 1 aDionysios Kehagias 1 aDimitrios Tzovaras 1 aErol Gelenbe 00 aA hierarchical model for quantifying software security based on static analysis alerts and software metrics 0 v29 3 aDespite the acknowledged importance of quantitative security assessment in secure software development, current literature still lacks an efcient model for measuring internal software security risk. To this end, in this paper, we introduce a hierarchical security assessment model (SAM), able to assess the internal security level of software products based on low-level indicators, i.e., security-relevant static analysis alerts and software metrics. The model, following the guidelines of ISO/IEC 25010, and based on a set of thresholds and weights, systematically aggregates these low-level indicators in order to produce a high-level security score that refects the internal security level of the analyzed software. The proposed model is practical, since it is fully automated and operationalized in the form of a standalone tool and as part of a broader Computer-Aided Software Engineering (CASE) platform. In order to enhance its reliability, the thresholds of the model were calibrated based on a repository of 100 popular software applications retrieved from Maven Repository. Furthermore, its weights were elicited in a way to chiefy refect the knowledge expressed by the Common Weakness Enumeration (CWE), through a novel weights elicitation approach grounded on popular decision-making techniques. The proposed model was evaluated on a large repository of 150 open-source software applications retrieved from GitHub and 1200 classes retrieved from the OWASP Benchmark. The results of the experiments revealed the capacity of the proposed model to reliably assess internal security at both product level and class level of granularity, with sufcient discretion power. They also provide preliminary evidence for the ability of the model to be used as the basis for vulnerability prediction. To the best of our knowledge, this is the frst fully automated, operationalized and sufciently evaluated security assessment model in the modern literature
01236nas a2200145 4500000000100000000000100001008004100002260000800043100002000051700001700071700001800088245005200106856005500158520087700213 2021 d bACM 1 aPiotr Fröhlich 1 aErol Gelenbe 1 aMateusz Nowak 00 aReinforcement Learning and Energy-Aware Routing uhttps://dl.acm.org/doi/pdf/10.1145/3472735.3473390 3 aWe present an approach that uses Reinforcement Learning (RL) with the Random Neural Network (RNN) acting as an adaptive critic, to route traffic in a SDN network, so as to minimize a composite Goal function that includes both packet delay and energy consumption per packet. We directly measure the traffic dependent energy consumption characeristics of the hardware that we use (including energy expended per packet) so as to parametrize the Goal function. The RL based algorithm with the RNN is implemented in a SDN controller that manages a multi-hop network which assigns service requests to specific servers so as to minimize the desired Goal. The overall system’s performance is evaluated through experimental measurements of packet delay and energy consumption under different traffic load values, demonstrating the effectiveness of the proposed approach.
01068nas a2200121 4500000000100000000000100001008004100002260003500043100001500078700001700093245008400110520075200194 2021 d c7-11 Dec 2021 bIEEE aBarcelona 1 aMert Nakip 1 aErol Gelenbe 00 aMIRAI Botnet Attack Detection with Auto-Associative Dense Random Neural Network 3 aInternet connected IoT devices have often been particularly vulnerable to Botnet attacks of the Mirai family in recent years. Thus we develop an attack detection scheme for Mirai Botnets, using the Auto-Associative Dense Random Neural Network that has recently been successful for other attacks such as the SYN attack. The resulting method is trained with normal traffic and tested with attack traffic, and shown to result in high accuracy detection of attacks with low false alarms. The approach is compared on the same data set with two other common Machine learning methods (Lasso and KNN) and shown to have higher accuracy, and much lower computation times than KNN and slightly higher (but comparable) computation times with respect to Lasso. 01535nas a2200145 4500000000100000000000100001008004100002260003900043100001700082700001500099700001800114700002400132245009800156520113500254 2021 d c11/2021 bIEEE aHouston, Texas, USA 1 aErol Gelenbe 1 aMert Nakip 1 aDariusz Marek 1 aTadeusz Czachórski 00 aDiffusion Analysis Improves Scalability of IoTNetworks to Mitigate the Massive Access Problem 3 aA significant challenge of IoT networks is to offer Quality of Service (QoS) and meet deadline requirements when packets from a massive number of IoT devices are forwarded to an IoT gateway. Many IoT devices tend to report their data to their wired or wireless network gateways at closely correlated instants of time, leading to congestion known as the MassiveAccess Problem (MAP), which increases the probability that the IoT data will not meet its required deadlines. Since IoT data loses much of its value if it arrives to destination beyond a required deadline, MAP has been extensively studied in the literature. Thus we first take a queueing theoretic view of the problem, and also use a Diffusion Approximation to gain insight into the IoT traffic statistics that affect MAP. Then we introduce the Quasi-Deterministic Transmission Policy (QDTP) which significantly alleviates MAP when the average traffic rate grows beyond a given level and substantially reduces the probability that IoT data deadlines are missed. The results are validated using real IoT data which has been placed in IP packets for transmission.
00581nas a2200109 4500000000100000000000100001008004100002100001700043245002900060490000700089520037500096 2021 d 1 aErol Gelenbe 00 aNOBEL PRIZE 50 YEARS AGO 0 v52 3 aAs we recall the major contributions of Professor Dennis Gabor that resulted in his Nobel Prize in Physics in 1971 for the invention of holography, it is interesting to put his impact on science, technology, as well as humanity, in a broader context so as to better understand his experiences, and recognize the very significant role that he played in his time.
16164nas a2200253 45000000001000000000001000010080041000022600021000431000025000647000022000897000024001117000023001357000025001587000020001837000020002037000021002237000026002447000029002707000017002997000023003167000022003392450090003615201545900451 2022 d c03/2022 aBelgium 1 aCharalampos Marantos 1 aMiltiadis Siavvas 1 aDimitrios Tsoukalas 1 aChristos Lamprakos 1 aLazaros Papadopoulos 1 aPaweł Boryszko 1 aKatarzyna Filus 1 aJoanna Domańska 1 aApostolos Ampatzoglou 1 aAlexander Chatzigeorgiou 1 aErol Gelenbe 1 aDionysios Kehagias 1 aDimitrios Soudris 00 aSDK4ED: One-click platform for Energy-aware, Maintainable and Dependable Applications 3 aDeveloping modern secure and low-energy applications in a short time imposes new challenges and creates the need of designing new software tools to assist developers in all phases of application development. The design of such tools cannot be considered a trivial task, as they should be able to provide optimization of multiple quality requirements. In this paper, we introduce the SDK4ED platform, which incorporates advanced methods and tools for measuring and optimizing maintainability, dependability and energy. The presented solution offers a complete tool-flow for providing indicators and optimization methods with emphasis on embedded software. Effective forecasting models and decision-making solutions are also implemented to improve the quality of the software, respecting the constraints imposed on maintenance standards, energy consumption limits and security vulnerabilities. The use of the SDK4ED platform is demonstrated in a healthcare embedded application.
00491nas a2200193 4500000000100000000000100001000000100002008004100003260004800044100002200092700002400114700001700138700002200155700001800177245006400195300000800259490000800267020002200275 2021 d bSpringer aVirtual Event, June 28–29, 2021 1 aAleksander Byrski 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aKrzysztof Grochla 1 aYuko Murayama 00 aComputer Science Protecting Human Society Against Epidemics a125 0 v616 a978-3-030-86582-5 01328nas a2200145 4500000000100000000000100001008004100002260002700043100001700070700001500087700001800102700002400120245006800144520097000212 2022 d bSpringer aNice, France 1 aErol Gelenbe 1 aMert Nakip 1 aDariusz Marek 1 aTadeusz Czachórski 00 aMitigating the Massive Access Problem in the Internet of Things 3 aThe traffic from the large number of IoT devices connected to the IoT is a source of congestion known as the Massive Access Problem (MAP), that results in packet losses, delays and missed deadlines for realtime data. This paper reviews the literature on MAP and summarizes recent results on two approaches that have been designed to mitigate MAP. One approach is based on randomizing the packet arrival instants to IoT gateways within a given time interval that is chosen so that packet arrivals do not exceed their deadlines, but also so that they do not constitute bulk arrivals. The second approach is a novel traffic shaping policy named the Quasi-Deterministic-Transmission-Policy (QDTP) which has been proved to drastically reduce queue formation at the receiving gateway by delaying packet departures from the IoT devices in a judicious manner. Both analytical and experimental results are summarized, that describe the benefits of these techniques.
01064nas a2200121 4500000000100000000000100001008004100002260002700043100001500070700001700085245006100102520077900163 2022 d bSpringer aNice, France 1 aMert Nakip 1 aErol Gelenbe 00 aBotnet Attack Detection with Incremental Online Learning 3 aIn recent years, IoT devices have often been the target of Mirai Botnet attacks. This paper develops an intrusion detection method based on Auto-Associated Dense Random Neural Network with incremental online learning, targeting the detection of Mirai Botnet attacks. The proposed method is trained only on benign IoT traffic while the IoT network is online; therefore, it does not require any data collection on benign or attack traffic. Experimental results on a publicly available dataset have shown that the performance of this method is considerably high and very close to that of the same neural network model with offline training. In addition, both the training and execution times of the proposed method are highly acceptable for real-time attack detection.
01547nas a2200193 4500000000100000000000100001008004100002260003900043100002200082700001700104700002400121700002400145700002200169700001500191700002300206700002300229245009800252520100300350 2022 d bIEEE aVilanova i la Geltrú, Spain 1 aMiltiadis Siavvas 1 aErol Gelenbe 1 aDimitrios Tsoukalas 1 aIlias Kalouptsoglou 1 aMaria Mathioudaki 1 aMert Nakip 1 aDionysios Kehagias 1 aDimitrios Tzovaras 00 aThe IoTAC Software Security-by-Design Platform: Concept, Challenges, and Preliminary Overview 3 aCritical everyday activities handled by modern IoT Systems imply that security is of major concern both for the end-users and the industry. Securing the IoT System Architecture is commonly used to strengthen its resilience to malicious attacks. However, the security of software running on the IoT must be considered as well, since the exploitation of its vulnerabilities can infringe the security of the overall system, regardless of how secure its architecture may be. Thus, we present an IoT Software Security-by-Design (SSD) Platform, which provides mechanisms for monitoring and optimizing the security of IoT software applications throughout their development lifecycle, to validate the broader security of the IoT software. This paper describes the proposed SSD platform that leverages security information from all phases of development, using some novel mechanisms that have been implemented, and which can lead to a holistic security evaluation and future security certification.
00999nas a2200133 4500000000100000000000100001008004100002260001200043100001700055700001500072700002400087245004500111520070900156 2022 d c08/2022 1 aErol Gelenbe 1 aMert Nakip 1 aTadeusz Czachórski 00 aImproving Massive Access to IoT Gateways 3 aIoT networks handle incoming packets from large numbers of IoT Devices (IoTDs) to IoT Gateways. This can lead to the IoT Massive Access Problem that causes buffer overflow, large end-to-end delays and missed deadlines. This paper analyzes a novel traffic shaping method named the Quasi-Deterministic Traffic Policy (QDTP) that mitigates this problem by shaping the incoming traffic without increasing the end-to-end delay or dropping packets. Using queueing theoretic techniques and extensive data driven simulations with real IoT datasets, the value of QDTP is shown as a means to considerably reduce congestion at the Gateway, and significantly improve the IoT network’s overall performance.
01295nas a2200121 4500000000100000000000100001008004100002260002300043100001700066700001500083245005800098520101700156 2022 d bIEEE aNice, France 1 aErol Gelenbe 1 aMert Nakip 00 aG-Networks Can Detect Different Types of Cyberattacks 3 aMalicious network attacks are a serious source of concern, and machine learning techniques are widely used to build Attack Detectors with off-line training with real attack and non-attack data, and used online to monitor system entry points connected to networks. Many machine learning based Attack Detectors are typically trained to identify specific types attacks, and the training of such algorithms to cover several types of attacks may be excessively time consuming. This paper shows that G-Networks, which are queueing networks with product form solution and special customers such as negative customers and triggers, can be trained just with “non-attack” traffic, can accurately detect several different attack types. This is established with a special case of G-Networks with triggerred customer movement. A DARPA attack and non-attack traffic repository is used to train and test the the G-Network, yielding comparable or clearly better accuracy than most known attack detection techniques.
01397nas a2200133 4500000000100000000000100001008004100002260012600043100001700169700001600186245005500202520098400257020002201241 2022 d c08/2022 bIEEE Xplore, CC 2022 - IEEE International Conference on Communications, 2022, pp. 2732-2737 aSeoul, South Korea 1 aErol Gelenbe 1 aKarl Sigman 00 aIoT Traffic Shaping and the Massive Access Problem 3 aIoT gateways aim to meet the deadlines and QoS needs of packets from as many IoT devices as possible, though this can lead to a form of congestion known as the Massive Access Problem (MAP). While much work was conducted on predictive or reactive scheduling schemes to match the arrival process of packets to the service capabilities of IoT gateways, such schemes may use substantial computation and communication between gateways and IoT devices. This paper proves that the recently proposed “Quasi-Deterministic-Transmission-Policy (QDTP)” traffic shaping approach which delays packets at IoT devices, substantially alleviates the MAP: QDTP does not increase overall end-to-end delay and reduces gateway queue length. We then introduce the Adaptive Non-Deterministic Transmission Policy (ANTP) that requires only one packet buffer at the gateway, offering substantial QoS improvement over FIFO scheduling.
a978-1-5386-8347-7 00269nas a2200109 4500000000100000000000100001008004100002260001400043100001300057700001700070245007200087 2022 d bIOS Press 1 aHuibo Bi 1 aErol Gelenbe 00 aSearching and Rescuing Victims in Emergency: A Comprehensive Survey 00385nas a2200145 4500000000100000000000100001008004100002260002000043100001700063700002000080700002300100700001600123700001800139245008200157 2022 d bSpringer Nature 1 aErol Gelenbe 1 aMarija Jankovic 1 aDionysios Kehagias 1 aAnna Marton 1 aAndras Vilmos 00 aSecurity in Computer and Information Sciences: Second International Symposium 00345nas a2200145 4500000000100000000000100001008004100002260001300043100001700056700001800073700002000091700001700111700002200128245004900150 2022 d bSpringer 1 aErol Gelenbe 1 aMateusz Nowak 1 aPiotr Fröhlich 1 aJerzy Fiolka 1 aJacek Chęciński 00 aEnergy, QoS and Security Aware Edge Services 00635nas a2200205 4500000000100000000000100001008004100002100001700043700001700060700001800077700002200095700001900117700002300136700002200159700001800181700002100199245007900220856010600299022002400405 2022 d 1 aErol Gelenbe 1 aGuy Brasseur 1 aLuc Cheffneux 1 aVéronique Dehant 1 aAnna Fabjanska 1 aVéronique Halloin 1 aMichel Judkiewicz 1 aVladimir Mrsa 1 aIgnacio Llorente 00 aChallenges for European Science and Technology Driven Innovation in Europe uhttps://www.euro-case.org/challenges-for-european-science-and-technology-driven-innovation-in-europe/ aReport to EURO-CASE 01281nas a2200157 4500000000100000000000100001008004100002260002700043100002400070700001700094700001900111700001800130245005000148490000900198520091600207 2022 d bSpringer aNice, France 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aGodlove Kuaban 1 aDariusz Marek 00 aOptimizing Energy Usage for an Electric Drone 0 v1596 3 aUnmanned Aerial Vehicles (UAVs) are rapidly gaining popu-
larity in a wide variety of applications, e.g., agriculture, health care, envi-
ronmental management, supply chains, law enforcement, surveillance,
and photography. Dones are often powered by batteries, making energy
a critical resource that must be optimised during the mission of the
drone. The duration of a done’s mission depends on the amount of energy
required to perform some manoeuvering actions (takeoff, level flight, hov-
ering, and landing), the energy required to power the ICT modules in
the drone, the drone’s speed, payload, and the wind. In this paper, we
present a model that minimizes the energy consumption of a low power
drone and maximizes the time required to completely drain the drone’s
battery and ensure the safe landing of the drone.
The complexity of battery-powered autonomous de-
vices such as Internet of Things (IoT) nodes or Unmanned Aerial
Vehicles (UAV) and the necessity to ensure an acceptable quality
of service, reliability, and security, have significantly increased
their energy demand. In this paper, we discuss using a diffusion
approximation process to approximate the dynamic changes in
the energy content of a battery. We consider the case when
energy harvesting sources are constantly charging the battery.
The model assumes a probabilistic consumption and delivery of
energy, giving the time-dependent distributions of the energy at
the battery, of the time remaining until it becomes empty, the
time required to charge the battery to its total capacity, or the
time it is operational between two moments of complete depletion.
When possible, we compare the diffusion approximation results
with corresponding Markovian models.
With the development of communication technologies and the increasing bandwidth of optical fibres and transmission speeds in current 5G and future 6G wireless networks, there is a growing demand for solutions organising traffic in such networks, taking into account both end-to-end transmissions and the possibility of data processing by edge services. The most pressing problems of today’s computer networks are not only bandwidth and transmission delays, but also security and energy consumption, which is becoming increasingly important in today’s climate. This paper presents a solution based on neural networks that organises network traffic taking into account the above criteria - quality of service (QoS), energy consumption and security.
00609nas a2200109 4500000000100000000000100001008004100002100001700043245002900060490000700089520040300096 2022 d 1 aErol Gelenbe 00 aNOBEL PRIZE 50 YEARS AGO 0 v52 3 aAs we recall the major contributions of Professor Dennis Gabor that resulted in his
Nobel Prize in Physics in 1971 for the invention of holography, it is interesting to put
his impact on science, technology, as well as humanity, in a broader context so as
to better understand his experiences, and recognize the very significant role that he
played in his time.
Recent advances in Command and Control (C2) modelling have developed
algorithmic representations of the command decision making process at both
the tactical (Rapid Planning) and operational (Deliberate Planning) levels of
command. In this paper, the development of methods by which indicators of
enemy/group behaviour can be extracted is discussed. The aim of this work
is to use the grouping techniques to establish, with more certainty, force size
and direction. We describe a multi-agent model approach, based on a
hierarchical framework, suitable for identifying indicators of collaborative
behaviour associated with enemy agile mission groups that commonly feature
in the modern Networked Era.
Classical Random Neural Networks (RNNs) have demonstrated effective applications in decision
making, signal processing, and image recognition tasks. However, their implementation has been
limited to deterministic digital systems that output probability distributions in lieu of stochastic be-
haviors of random spiking signals. We introduce the novel class of supervised Random Quantum
Neural Networks (RQNNs) with a robust training strategy to better exploit the random nature of
the spiking RNN. The proposed RQNN employs hybrid classical-quantum algorithms with super-
position state and amplitude encoding features, inspired by quantum information theory and the
brain’s spatial-temporal stochastic spiking property of neuron information encoding. We have ex-
tensively validated our proposed RQNN model, relying on hybrid classical-quantum algorithms via
the PennyLane Quantum simulator with a limited number of qubits. Experiments on the MNIST,
FashionMNIST, and KMNIST datasets demonstrate that the proposed RQNN model achieves an av-
erage classification accuracy of 94.9%. Additionally, the experimental findings illustrate the proposed
RQNN’s effectiveness and resilience in noisy settings, with enhanced image classification accuracy
when compared to the classical counterparts (RNNs), classical Spiking Neural Networks (SNNs), and
the classical convolutional neural network (AlexNet). Furthermore, the RQNN can deal with noise,
which is useful for various applications, including computer vision in NISQ devices. The PyTorch
code 1 is made available on GitHub to reproduce the results reported in this manuscript.
A novel online Compromised Device Identification System (CDIS) is presented to identify IoT devices and/or IP addresses that are compromised by a Botnet attack, within a set of sources and destinations that transmit packets. The method uses specific metrics that are selected for this purpose and which are easily extracted from network traffic, and trains itself online during normal operation with an Auto-Associative Dense Random Neural Network (AADRNN) using traffic metrics measured as traffic arrives. As it operates, the AADRNN is trained with auto-associative learning only using traffic that it estimates as being benign, without prior collection of different attack data. The experimental evaluation on publicly available Mirai Botnet attack data shows that CDIS achieves high performance with Balanced Accuracy of 97%, despite its low on-line training and execution time. Experimental comparisons show that the AADRNN with sequential (online) auto-associative learning, provides the best performance among six different state-of-the-art machine learning models. Thus CDIS can provide crucial effective information to prevent the spread of Botnet attacks in IoT networks having multiple devices and IP addresses.
01293nas a2200133 4500000000100000000000100001008004100002260003900043100002400082700001700106700001900123245007900142520093800221 2023 d c03/2023 bIEEE Xplore aNice, France 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aGodlove Kuaban 00 aModelling energy changes in the energy harvesting battery of an IoT device 3 aThe complexity of battery-powered autonomous devices such as Internet of Things (IoT) nodes or Unmanned Aerial Vehicles (UAV) and the necessity to ensure an acceptable quality of service, reliability, and security have significantly increased their energy demand. In this paper, we discuss using a diffusion approximation process to approximate the dynamic changes in the energy content of a battery. We consider the case when energy harvesting sources are constantly charging the battery. The model assumes a probabilistic consumption and delivery of energy, giving the time-dependent distributions of the energy at the battery, of the time remaining until it becomes empty, the
time required to charge the battery to its total capacity, or the time it is operational between two moments of complete depletion.
When possible, we compare the diffusion approximation results with corresponding Markovian models.
Unmanned Aerial Vehicles (UAVs) are rapidly gaining popularity in a wide variety of applications, e.g., agriculture, health care, environmental management, supply chains, law enforcement, surveillance, and photography. Dones are often powered by batteries, making energy
a critical resource that must be optimised during the mission of the drone. The duration of a done’s mission depends on the amount of energy required to perform some manoeuvering actions (takeoff, level flight, hovering, and landing), the energy required to power the ICT modules in the drone, the drone’s speed, payload, and the wind. In this paper, we present a model that minimizes the energy consumption of a low-power drone and maximizes the time required to completely drain the drone’s battery and ensure the safe landing of the drone.
This paper presents several novel algorithms for real-time cyberattack detection using the Auto-Associative Deep Random Neural Network, which were developed in the HORIZON 2020 IoTAC Project. Some of these algorithms require offline learning, while others require the algorithm to learn during its normal operation while it is also testing the flow of incoming traffic to detect possible attacks. Most of the methods we present are designed to be used at a single node, while one specific method collects data from multiple network ports to detect and monitor the spread of a Botnet. The evaluation of the accuracy of all the methods is carried out with real attack traces. These novel methods are also compared with other state-of-the-art approaches, showing that they offer better or equal performance,
01089nas a2200145 4500000000100000000000100001008004100002260003800043100002400081700001500105700001700120245008300137856005000220520067300270 2023 d bIEEE aDaytona Beach, Florida, USA 1 aMohammed Nasereddin 1 aMert Nakip 1 aErol Gelenbe 00 aMeasurement Based Evaluation and Mitigation of Flood Attacks on a LAN Test-Bed uhttps://ieeexplore.ieee.org/document/10223358 3 aThe IoT is vulnerable to network attacks, and Intrusion Detection Systems (IDS) can provide high attack detection accuracy and are easily installed in IoT Servers. However, IDS are seldom evaluated in operational conditions which are seriously impaired by attack overload. Thus a Local Area Network testbed is used to evaluate the impact of UDP Flood Attacks on an IoT Server, whose first line of defence is an accurate IDS. We show that attacks overload the multi-core Server and paralyze its IDS. Thus a mitigation scheme that detects attacks rapidly, and drops packets within milli-seconds after the attack begins, is proposed and experimentally evaluated.
01516nas a2200133 4500000000100000000000100001008004100002260002100043100001700064700002400081245009800105856005900203520112000262 2023 d bIEEE aExeter, UK 1 aErol Gelenbe 1 aMohammed Nasereddin 00 aProtecting IoT Servers Against Flood Attacks with the Quasi Deterministic Transmission Policy uhttps://ieeexplore.ieee.org/abstract/document/10538961 3 aServers at Supply Chains that receive packets from IoT devices should meet the QoS needs of incoming packets, and protect the system from Cyberattacks. UDP Floods are often part of Cyberattacks that overwhelm Supply Chains and the IoT through congestion that paralyzes their operation and limits their ability for timely Attack Detection.Thus this paper proposes an architecture that protects a connected Server using a Smart Quasi-Deterministic Transmission Policy Forwarder placed at its input port. This Forwarder shapes the incoming traffic, sends it to the Server without modifying the overall packet delay, and avoids Server congestion. The relevant theoretical background is reviewed, and measurements during a UDP Flood Attack are provided to compare the Server performance, with and without the Forwarder. It is seen that during a UDP Flood Attack, the Forwarder protects the Server from congestion allowing it to effectively identify Attack Packets. The resulting Forwarder congestion is also eliminated with “drop” commands generated by the Forwarder, or sent by the Server to the Forwarder.
01410nas a2200133 4500000000100000000000100001008004100002260000900043100001500052700001500067700001700082245009000099520108700189 2024 d bIEEE 1 aMert Nakip 1 aBaran Gül 1 aErol Gelenbe 00 aDecentralized Online Federated G-Network Learning for Lightweight Intrusion Detection 3 aCyberattacks are increasingly threatening networked systems, often with the emergence of new types of unknown (zero-day) attacks and the rise of vulnerable devices. While Machine Learning (ML)-based Intrusion Detection Systems (IDSs) have been shown to be extremely promising in detecting these attacks, the need to learn large amounts of labeled data often limits the applicability of ML-based IDSs to cybersystems that only have access to private local data. To address this issue, this paper proposes a novel Decentralized and Online Federated Learning Intrusion Detection (DOF-ID) architecture. DOF-ID is a collaborative learning system that allows each IDS used for a cybersystem to learn from experience gained in other cybersystems in addition to its own local data without violating the data privacy of other systems. As the performance evaluation results using public Kitsune and Bot-IoT datasets show, DOF-ID significantly improves the intrusion detection performance in all collaborating nodes simultaneously with acceptable computation time for online learning.
02474nas a2200157 4500000000100000000000100001008004100002260001300043100001500056700001700071245007300088856005900161490000700220520207500227022001402302 2024 d c05/2024 1 aMert Nakip 1 aErol Gelenbe 00 aOnline Self-Supervised Deep Learning for Intrusion Detection Systems uhttps://ieeexplore.ieee.org/abstract/document/10531267 0 v19 3 aThis paper proposes a novel Self-Supervised Intrusion Detection (SSID) framework, which enables a fully online {Deep Learning (DL)} based Intrusion Detection System (IDS) that requires no human intervention or prior off-line learning. The proposed framework analyzes and labels incoming traffic packets based only on the decisions of the IDS itself using an Auto-Associative Deep Random Neural Network, and on an online estimate of its statistically measured trustworthiness. The SSID framework enables IDS to adapt rapidly to time-varying characteristics of the network traffic, and eliminates the need for offline data collection. This approach avoids human errors in data labeling, and human labor and computational costs of model training and data collection. The approach is experimentally evaluated on public datasets and compared with well-known {machine learning and deep learning} models, showing that this SSID framework is very useful and advantageous as an accurate and online learning DL-based IDS for IoT systems.
This paper proposes a novel Self-Supervised Intrusion Detection (SSID) framework, which enables a fully online Deep Learning (DL) based Intrusion Detection System (IDS) that requires no human intervention or prior off-line learning. The proposed framework analyzes and labels incoming traffic packets based only on the decisions of the IDS itself using an Auto-Associative Deep Random Neural Network, and on an online estimate of its statistically measured trustworthiness. The SSID framework enables IDS to adapt rapidly to time-varying characteristics of the network traffic, and eliminates the need for offline data collection. This approach avoids human errors in data labeling, and human labor and computational costs of model training and data collection. The approach is experimentally evaluated on public datasets and compared with well-known machine learning and deep learning models, showing that this SSID framework is very useful and advantageous as an accurate and online learning DL-based IDS for IoT systems.
a1556-6013 02144nas a2200169 4500000000100000000000100001008004100002260001200043100001900055700001700074700002400091700002000115700001800135245012900153490000700282520168500289 2023 d c07/2023 1 aGodlove Kuaban 1 aErol Gelenbe 1 aTadeusz Czachórski 1 aPiotr Czekalski 1 aJulius Tangka 00 aModelling of the Energy Depletion Process and Battery Depletion Attacks for Battery-Powered Internet of Things (IoT) Devices 0 v23 3 aThe Internet of Things (IoT) is transforming almost every industry, including agriculture, food processing, health care, oil and gas, environmental protection, transportation and logistics, manufacturing, home automation, and safety. Cost-effective, small-sized batteries are often used to power IoT devices being deployed with limited energy capacity. The limited energy capacity of IoT devices makes them vulnerable to battery depletion attacks designed to exhaust the energy stored in the battery rapidly and eventually shut down the device. In designing and deploying IoT devices, the battery and device specifications should be chosen in such a way as to ensure a long lifetime of the device. This paper proposes diffusion approximation as a mathematical framework for modelling the energy depletion process in IoT batteries. We applied diffusion or Brownian motion processes to model the energy depletion of a battery of an IoT device. We used this model to obtain the probability density function, mean, variance, and probability of the lifetime of an IoT device. Furthermore, we studied the influence of active power consumption, sleep time, and battery capacity on the probability density function, mean, and probability of the lifetime of an IoT device. We modelled ghost energy depletion attacks and their impact on the lifetime of IoT devices. We used numerical examples to study the influence of battery depletion attacks on the distribution of the lifetime of an IoT device. We also introduced an energy threshold after which the device's battery should be replaced in order to ensure that the battery is not completely drained before it is replaced.01732nas a2200109 4500000000100000000000100001008004100002100001700043700001500060245008300075520146400158 2023 d 1 aErol Gelenbe 1 aMert Nakip 00 aIoT Network Cybersecurity Assessment with the Associated Random Neural Network 3 a
This paper proposes a method to assess the security of an n device, or IP address, IoT network by simultaneously identifying all the compromised IoT devices and IP addresses. It uses a specific Random Neural Network (RNN) architecture composed of two mutually interconnected sub-networks that complement each other in a recurrent structure, called the Associated RNN (ARNN). For each of the n devices or IP addresses in the IoT network, two distinct neurons of the ARNN advocate opposite views: compromised or not compromised. The fully interconnected 2 n neuron ARNN structure of paired neurons learns offline from ground truth data. Thus rather than requiring a separate attack detector at each network node, the ARNN offers a single overall attack detector that observes the incoming traffic at each node, learns about the interdependencies between network nodes, and formulates a recommendation for each device or IP address in an IoT network. The ARNN weight initialization and learning algorithm are discussed, and the ARNN performance is evaluated using real attack data, and compared against several learning and testing techniques. Results are obtained both for off-line learning with ground truth data, and for on-line incremental learning using a simplified average metric measured from incoming packet traffic. Comparisons with the best state-of-the-art techniques show that the ARNN significantly outperforms previously known approaches.
01516nas a2200133 4500000000100000000000100001008004100002260001200043100001700055245006800072490000900140520121900149022001401368 2023 d c08/2023 1 aErol Gelenbe 00 aElectricity Consumption by ICT: Facts, trends, and measurements 0 v2023 3 aThis paper considers key issues surrounding energy consumption by information and communication technologies (ICT), which has been steadily growing and is now attaining approximately 10% of the worldwide electricity consumption with a significant impact on greenhouse gas emissions. The perimeter of ICT systems is discussed, and the role of the subsystems that compose ICT is considered. Data from recent years is used to understand how each of these sub-systems contribute to ICT's energy consumption. The quantitatively demonstrated positive correlation between the penetration of ICT in the world's different economies and the same economies' contributions to undesirable greenhouse gas emissions is also discussed. The paper also examines how emerging technologies such as 5G, AI, edge computing, and cryptocurrencies are contributing to the worldwide increase in electricity consumption by ICT, despite the increases in ICT efficiency, in terms of energy consumed per bit processed, stored, or transmitted. The measurement of specific ICT systems' electricity consumption is also addressed, and the manner in which this consumption can be minimized in a specific edge computing context is discussed.
a1530-2180 01685nas a2200145 4500000000100000000000100001008004100002260004300043100001900086700001700105700002400122700002000146245009000166520128300256 2024 d c16/01/2024 bIEEE aStony Brook, NY, USA 1 aGodlove Kuaban 1 aErol Gelenbe 1 aTadeusz Czachórski 1 aPiotr Czekalski 00 aModelling the Energy Performance of Off-Grid Sustainable Green Cellular Base Stations 3 aThere is a growing awareness of the need to reduce carbon emissions from the operation of mobile networks. The massive deployment of ultra-dense 5G and IoT networks will significantly increase energy demand and put the electricity grid under stress while also driving up operational costs. In this paper, we model the energy performance of an off-grid sustainable green cellular base station site which consists of a solar power system, Battery Energy Storage (BESS) and Hydrogen Energy Storage (HESS) system, and various types of macrocells, microcells, picocells, or femtocells, with broadband optical or microwave transmission systems, and other electrical and electronic systems (air conditioner, power converters, and controllers. We propose diffusion-based models of the charging and discharging processes of the energy storage systems, and obtain the probability of charging them to their full capacities during the day and completely discharging them at the end of each day. We also investigate the impact of design parameters such as the mean charging rate and the mean discharging rate on the probability densities of charging BESS and HESS to their full capacities during the day and of completely discharging them before the end of each night period.01666nas a2200169 4500000000100000000000100001008004100002260000900043100001900052700002400071700001700095700001800112700002000130245009500150520122900245020002201474 2024 d bIEEE 1 aGodlove Kuaban 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aSapana Sharma 1 aPiotr Czekalski 00 aA Markov model for a Self-Powered Green IoT Device with State-Dependent Energy Consumption 3 a
The growing concerns about the resource sustainability and environmental impact of deploying billions or trillions of IoT devices in the various sectors of society or economy have given rise to an energy research field known as Green-IoT (G-IoT). G-IoT emphasises the need to prioritise energy efficiency and the reduction of pollution in the IoT ecosystem. This paper presents a Markov model for a self-powered Green IoT device with state-dependent energy consumption. The model evaluates the energy performance of a self-powered Green IoT device. We derive the probability distribution of the energy content of the battery, including the probability that the energy stored in the battery is completely depleted and the density of the lifetime of the IoT device. We investigate the influence of some design parameters on the results. Introducing the threshold at which the device is forced to enter an energy-saving regime significantly increases a G-IoT device's lifetime. Apart from deploying energy-efficient mechanisms to increase the lifetime of a G-IoT device, integrating an efficient energy harvesting system into the device will increase its lifetime and reduce the frequency of battery replacement.a979-8-3503-3691-7 02099nas a2200181 4500000000100000000000100001008004100002260004000043100001900083700002400102700001700126700001600143700001900159700002000178245008400198520161300282020002201895 2024 d c17/07/2024 bIEEE aAyia Napa, Cyprus 1 aGodlove Kuaban 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aPiotr Pecka 1 aValery Nkemeni 1 aPiotr Czekalski 00 aEnergy performance of Internet of Things (IoT) networks for pipeline monitoring 3 a
Pipelines are the most convenient ways to transport fluids (e.g., water, oil, and gas). However, leakage of fluids into the environment results in resource wastage (especially water, which is becoming a scarce resource) and environmental pollution (in the case of leakage of toxic fluids like oil and gas). Emerging technologies like the Internet of Things (IoT), Wireless Sensor Networks (WSNs), Artificial Intelligence (AI), distributed computing, and cloud computing enable continuous monitoring of pipelines to detect leakages and corrosion on the pipeline. The main challenge with using battery-powered sensor nodes to monitor pipelines is the energy constraint, necessitating frequent battery replacement. Thus, there is a need to develop energy-saving mechanisms to prolong the lifetime of these sensor nodes. In this paper, we use the diffusion approximation modelling framework in which the data from the experimental testbed are used to model the dynamics of the battery's energy content and to estimate the mean and variance of the device's lifetime. The novelty in the proposed diffusion model of the battery of an IoT node is the introduction of multiple energy thresholds that split the energy state-space of the battery into multiple energy-saving regimes. As the battery discharges, the node gradually transitions into energy-saving regimes by reconfiguring some of its parameters to reduce energy consumption (sometimes at the cost of trading off some performance metrics). We investigate the impact of energy-saving regimes or the number of thresholds on the node's lifetime.a979-8-3503-6126-1 01486nas a2200169 4500000000100000000000100001008004100002260001500043100001900058700001700077700002400094700002000118700001900138245006400157490000800221520108700229 2024 d c10/07/2024 1 aGodlove Kuaban 1 aErol Gelenbe 1 aTadeusz Czachórski 1 aPiotr Czekalski 1 aValery Nkemeni 00 aEnergy Performance of Off-Grid Green Cellular Base Stations 0 v165 3 a
The most energy-hungry parts of mobile networks are the base station sites, which consume around $60-80\%$ of their total energy. One of the approaches for relieving this energy pressure on the electricity grid infrastructure and reducing the Operational Expenditures (OPEX) is to power base stations with renewable energy. However, the design of a green mobile network requires the dimensioning of the energy harvesting and storage systems through the estimation of the network's energy demand. Therefore, this paper develops a diffusion-based modelling framework for solar-powered green off-grid base station sites. We apply this framework to evaluate the energy performance of homogeneous and hybrid energy storage systems supplied by harvested solar energy. We present the complete analysis, with numerical examples, to study the relationship between the design parameters and the energy performance metrics. The numerical computations demonstrate how the proposed framework can be applied to evaluate homogeneous and unconventional hybrid energy storage systems.00838nas a2200289 4500000000100000000000100001008004100002100002200043700002400065700002500089700002500114700002300139700001700162700002100179700002100200700002600221700002000247700002100267700001900288700002600307700002200333700002900355700001700384700002300401700002300424245010100447 2024 d 1 aMiltiadis Siavvas 1 aDimitrios Tsoukalas 1 aCharalambos Marantos 1 aLazaros Papadopoulos 1 aChristos Lamprakos 1 aOliviu Matei 1 aChristos Strydis 1 aMuhammad Siddiqi 1 aPhilippe Chrobocinski 1 aKatarzyna Filus 1 aJoanna Domańska 1 aParis Avgeriou 1 aApostolos Ampatzoglou 1 aDimitrios Soudris 1 aAlexander Chatzigeorgiou 1 aErol Gelenbe 1 aDionysios Kehagias 1 aDimitrios Tzovaras 00 aSDK4ED: a platform for building energy efficient, dependable, and maintainable embedded software 01745nas a2200145 4500000000100000000000100001008004100002260001200043100001700055700001500072700002200087245005800109490000800167520142400175 2024 d c10/2024 1 aErol Gelenbe 1 aMert Nakip 1 aMiltiadis Siavvas 00 aSystem-wide vulnerability of multi-component software 0 v196 3 a
In software systems comprised of many interconnected components, the vulnerability of each component will affect the vulnerability of other components and of the system as a whole. Existing techniques allow the quantification of the vulnerability of individual components taken singly, but the assessment of their vulnerability when they are interconnected or interdependent remains a challenge. The present work addresses this problem with a novel System-Wide Vulnerability Assessment (SWVA) framework for interconnected software components, based on an Associated Random Neural Network (ARNN) that estimates the system-wide vulnerability of all software components from known local vulnerabilities of individual components, and from their interconnections. The ARNN uses a problem-specific weight initialization, and learns from existing software system examples with a gradient-based deep learning algorithm. The ARNN is then used to assess the vulnerability of hitherto unseen software systems. The performance of the proposed ARNN-based SWVA framework is evaluated and compared against several well-known machine learning techniques on 13 different versions of a real-world software system with up to 11 components. The experimental results show the superior performance of the ARNN achieving above 85% median accuracy and good high scalability with respect to the number of connected software components.
02510nas a2200145 4500000000100000000000100001008004100002260001200043100001700055700001500072700001500087245016100102490000700263520209400270 2024 d c12/2024 1 aErol Gelenbe 1 aBaran Gül 1 aMert Nakip 00 aDISFIDA: Distributed Self-Supervised Federated Intrusion Detection Algorithm with Online Learning for Health Internet of Things and Internet of Vehicles 0 v28 3 aNetworked health systems are often the victims of cyberattacks with serious consequences for patients and healthcare costs, with the Internet of Things (IoT) being an additional prime target. In future systems we can imagine that the Internet of Vehicles (IoV) will also be used for conveying patients for diagnosis and treatment in an integrated manner. Thus the medical field poses very significant and specific challenges since even for a single patient, several providers may carry out tests or offer healthcare services, and may have distinct interconnected sub-contractors for services such as ambulances and connected cars, connected devices or temporary staff providers, that have distinct confidentiality requirements on top of possible commercial competition. On the other hand, these distinct entities can be subject to similar or coordinated attacks, and could benefit from each others’ cybersecurity experience to better detect and mitigate cyberattacks. Thus the present work proposes a novel Distributed Self-Supervised Federated Intrusion Detection Algorithm (DISFIDA), with Online Self-Supervised Federated Learning, that uses Dense Random Neural Networks (DRNN). In DISFIDA learning data is private, and neuronal weights are shared among Federated partners. Each partner in DISFIDA combines its synaptic weights with those it receives other partners, with a preference for those weights that have closer numerical values to its own weights which it has learned on its own. DISFIDA is tested with three open-access datasets against five benchmark methods, for two relevant IoT healthcare applications: networks of devices (e.g., body sensors), and Connected Smart Vehicles (e.g., ambulances that transport patients). These tests show that the DISFIDA approach offers 100% True Positive Rate for attacks (one percentage point better than comparable state of the art methods which attain 99%) so that it does better at detecting attacks, with 99% True Negative Rate similar to state-of-the-art Federated Learning, for Distributed Denial of Service (DDoS) attacks.
01127nas a2200145 4500000000100000000000100001008004100002260002500043100002400068700001500092700001700107245006800124856005900192520073000251 2024 d bIEEE aKrakow, Poland 1 aMohammed Nasereddin 1 aMert Nakip 1 aErol Gelenbe 00 aDeep Learning Intrusion Detection and Mitigation of DoS Attacks uhttps://ieeexplore.ieee.org/abstract/document/10786560 3 aInternet of Things (IoT) networks are highly vulnerable to common network DoS and DDoS attacks, which flood limited system resources or IoT devices, overwhelming them with large numbers of attack packets. In order to mitigate such attacks, this paper develops a lightweight yet effective Intrusion Detection and Prevention System (IDPS), that sequentially detects and mitigates the attack via a Deep Random Neural Network (DRNN) and a Drop-Idle-Repeat process. The IDPS is evaluated for UDP Floods, attacks on an experimental test-bed. The results show that UDP Flood attacks can be mitigated with the proposed IDPS, allowing the system to continue routine operations, and resume communications when the attack ends.
02260nas a2200205 4500000000100000000000100001008004100002260001500043100001900058700002400077700001700101700001600118700001800134700001800152700001900170700002000189245005500209490000700264520178300271 2024 d c07/11/2024 1 aGodlove Kuaban 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aPiotr Pecka 1 aSapana Sharma 1 aPradeep Singh 1 aValery Nkemeni 1 aPiotr Czekalski 00 aEnergy performance of self-powered Green IoT nodes 0 v12 3 aThe widespread adoption of the Internet of Things (IoT) partly depends on the successful design and deployment of IoT nodes that can operate for several years without any service outage and the need to replace their energy storage systems (e.g., battery, capacitor, or supercapacitor) when all the stored energy is depleted or when the cycle life of the Energy Storage Systems (ESS) is reached. Replacing batteries in the case of large-scale IoT networks and nodes located in places that are hard to reach is very challenging and costly, requiring the design of IoT nodes that can operate for several years without the need for human intervention. One such example is the deployment of IoT nodes in large agricultural fields (for soil or crop monitoring) or a long-distance pipeline (for pipeline monitoring). This paper investigates the practical implications of imposing energy-saving thresholds on the energy performance metrics of green IoT nodes. We propose an energy packet-based model for the evaluation of the energy performance of a green IoT node with the possibility of switching the node to energy-saving regimes on the fly when the energy content of the ESS reaches defined thresholds. Configuring single or multiple thresholds improves the energy performance of the node significantly (e.g., increases the lifetime of the node and reduces the probability of service outage and energy wastage), and the value of the threshold(s) should be carefully chosen. The energy performance of the IoT node can also be improved by dimensioning the energy harvesting system to ensure that the node operates for several years without running out of energy (e.g., maximizing the lifetime of the nodes and minimising the probability of service outage and energy wastage).
01664nas a2200181 4500000000100000000000100001008004100002260002500043100001900068700002400087700001700111700001600128700001900144700002000163245007400183856005100257520117400308 2024 d bIEEE aKrakow, Poland 1 aGodlove Kuaban 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aPiotr Pecka 1 aValery Nkemeni 1 aPiotr Czekalski 00 aImpact of energy leakage on the energy performance of green IoT nodes uhttps://ieeexplore.ieee.org/document/10786519 3 aIn the present paper, we investigate the impact of imperfections (non-idealities) of the energy storage system (e.g., batteries, capacitors, or supercapacitors) on the energy performance of green IoT nodes. In particular, we investigate the impact of energy leakage from the Energy Storage System (ESS) on important energy performance metrics, such as service outage probability, the density of the lifetime of the node, and the time-dependent mean number of energy packets (EPs) in the ESS. Also, we explore various strategies that can be employed to compensate for the impact of energy losses due to energy leakage on the energy performance metrics. Specifically, we examine two potential strategies for improving the energy performance of the IoT nodes: (i) through increasing the energy generation rate of the energy harvesters (e.g., by adding additional solar panels or replacing the existing solar panels with more efficient ones that can produce more energy), and (ii) through reducing the energy consumption rate of the IoT node (e.g., by configuring ESS energy thresholds below which the nodes are forced to operate in low energy consumption states).
01641nas a2200145 4500000000100000000000100001008004100002100001800043700001400061700001400075700001700089245004400106856011400150520123100264 2024 d 1 aGuoliang Zhou 1 aYijia Liu 1 aZheng Yan 1 aErol Gelenbe 00 aIs ChatGPT Trustworthy Enough? A Review uhttps://www.researchgate.net/publication/385065063_Is_ChatGPT_Trustworthy_Enough_A_Review#fullTextFileContent 3 aChatGPT, as an advanced model that seamlessly integrates into diverse digital interactions,
shows great potential to enhance the performance of consumer technology and reshape its
landscape. The critical question of its trustworthiness and associated challenges becomes
increasingly prominent. However, the literature still lacks a thorough review to study its trust. This
comprehensive review explores whether ChatGPT is trustworthy enough by navigating the
multifaceted realm of large language models, placing a significant focus on its exemplar model,
ChatGPT. Our exploration traverses the complex interplay of factors impacting user trust, including
trust in ChatGPT itself and trust in its utilization and dissemination. By delving into insightful
perspectives on the ChatGPT trustworthiness regarding a set of evaluation criteria including
fundamental properties, subjective properties, security and privacy, we find that ChatGPT is far from
trustworthy based on related literature review. This paper sheds light on the weakness of ChatGPT
trust and provides the trajectory of future development in the realm of large language models
We analyze, further and deeper, a recently proposed
technique for addressing the Massive Access Problem (MAP), an
issue in telecommunications which arises when too many devices
transmit packets to a gateway in quick succession. This technique,
the Adaptive-Quasi-Deterministic Transmission Policy (AQDTP)
is a special case of “traffic shaping” which involves delaying
some packets at the points of origin to alleviate congestion at
the routers. One nice feature of AQDTP is that it loses no
packets and allows an infinite buffer. In this work, to clarify
the approach in a general queueing theory framework, and to
move beyond the original telecommunications application, we
frame these potential delays as time spent at a caf´e by customers
before proceeding to a service facility. We first present some
sample-path results that significantly refine and expand upon
what was shown in previous work, and then present further
results under a general stationary ergodic stochastic framework.
In the sample-path realm, we give conditions that ensure AQDTP
will not change the total delay and sojourn time of any customer
as compared to what that customer would have experienced if
there was no caf´e; but we also prove that AQDTP can never
reduce the total delay. The difference is that, under AQDTP, some
of that delay is spent at the caf´e instead of in the queue/line at the
service facility. In a stochastic framework, our focus is on stability
and constructing proper stationary versions of the model. Under
i.i.d. assumptions we dig deeper by proving Harris recurrence
of an underlying two-dimensional Markov process, and explicitly
find positive recurrent regeneration points.
Cruise ships and other naval vessels include automated Internet of Things (IoT)-based evacuation systems for the passengers and crew to assist them in case of emergencies and accidents. The technical challenges of assisting passengers and crew to safety during emergencies include various aspects such as sensor failures, imperfections in the sound or display systems that are used to direct evacuees, the timely selection of optimum evacuation routes for the evacuees, as well as computation and communication delays that may occur in the IoT infrastructure due to intense activities during an emergency. In addition, during an emergency, the evacuees may be confused or in a panic, and may make mistakes in following the directions offered by the evacuation system. Therefore, the purpose of this work is to analyze the effect of two important aspects that can have an adverse effect on the passengers’ evacuation time, namely (a) the computer processing and communication delays, and (b) the errors that may be made by the evacuees in following instructions. The approach we take uses simulation with a representative existing cruise ship model, which dynamically computes the best exit paths for each passenger, with a deadline-driven Adaptive Navigation Strategy (ANS). Our simulation results reveal that delays in the evacuees’ reception of instructions can significantly increase the total time needed for passenger evacuation. In contrast, we observe that passenger behavior errors also affect the evacuation duration, but with less effect on the total time needed to evacuate passengers. These findings demonstrate the importance of the design of passenger evacuation systems in a way that takes into account all realistic features of the ship’s indoor evacuation environment, including the importance of having high-performance data processing and communication systems that will not result in congestion and communication delays.
00304nas a2200121 4500000000100000000000100001008004100002260003800043100001400081700001700095700001600112245005400128 2024 d c11/2024 bIEEE aOttawa, ON, Canada 1 aYuting Ma 1 aErol Gelenbe 1 aKezhong Liu 00 aIoT Performance for Maritime Passenger Evacuation 01596nas a2200193 4500000000100000000000100001008004100002260001200043100001800055700001500073700001400088700001100102700001900113700001700132700001700149245009100166490000800257520113700265 2024 d c12/2024 1 aMinqiang Yang 1 aEdith Ngai 1 aXiping Hu 1 aBin Hu 1 aJiangchuan Liu 1 aErol Gelenbe 1 aVictor Leung 00 aDigital Phenotyping and Feature Extraction on Smartphone Data for Depression Detection 0 v112 3 aSmartphones are widely used as portable data collectors for wearable and healthcare sensors that can passively collect data streams related to the environment, health status, and behaviors. Recent research shows that the collected data can be used to monitor not only the physical states but also the mental health of individuals. However, extracting the features of digital phenotypes that characterize major depressive disorder (MDD) is technically challenging and may raise significant privacy concerns. Addressing such challenges has become the focus of many researchers. This article provides a comprehensive analysis of several key issues related to ubiquitous sensing to aid in detecting MDD. Specifically, this article analyzes existing methodologies and feature extraction algorithms used to detect possible MDD through digital phenotyping from smartphone data. In particular, five types of features are summarized and explained, namely, location, movement, rhythm, sleep, and social and device usage. Finally, related limitations and challenges are discussed to provide paths for further research and engineering.
02029nas a2200133 4500000000100000000000100001008004100002260001200043100001700055245005500072856004400127490000700171520171700178 2025 d c02/2025 1 aErol Gelenbe 00 aMinimizing delay and power consumption at the edge uhttps://www.mdpi.com/1424-8220/25/2/502 0 v25 3 aEdge computing systems must offer low latency at low cost and low power consumption for sensors and other applications, including the IoT, smart vehicles, smart homes, and 6G. Thus, substantial research has been conducted to identify optimum task allocation schemes in this context using non-linear optimization, machine learning, and market-based algorithms. Prior work has mainly focused on two methodologies: (i) formulating non-linear optimizations that lead to NP-hard problems, which are processed via heuristics, and (ii) using AI-based formulations, such as reinforcement learning, that are then tested with simulations. These prior approaches have two shortcomings: (a) there is no guarantee that optimum solutions are achieved, and (b) they do not provide an explicit formula for the fraction of tasks that are allocated to the different servers to achieve a specified optimum. This paper offers a radically different and mathematically based principled method that explicitly computes the optimum fraction of jobs that should be allocated to the different servers to (1) minimize the average latency (delay) of the jobs that are allocated to the edge servers and (2) minimize the average energy consumption of these jobs at the set of edge servers. These results are obtained with a mathematical model of a multiple-server edge system that is managed by a task distribution platform, whose equations are derived and solved using methods from stochastic processes. This approach has low computational cost and provides simple linear complexity formulas to compute the fraction of tasks that should be assigned to the different servers to achieve minimum latency and minimum energy consumption.
01576nas a2200157 4500000000100000000000100001008004100002260001200043100001700055700001500072700002200087245008900109856007600198490000700274520113700281 2024 d c09/2024 1 aErol Gelenbe 1 aMert Nakip 1 aMiltiadis Siavvas 00 aSystem Wide Vulnerability and Trust in Multi-Component Communication System Software uhttps://drive.google.com/file/d/1123-TES0XmyG8_l1XlmAfdWV5Jf6QPeo/view 0 v39 3 aIn the software rich environment of 6G, systems will be surrounded by edge devices that support distributed software systems which are critical to operations. Such systems may also be subject to frequent updates or uploads of individual software components. Trust in such systems will therefore depend on our ability to rapidly ensure that such software is not vulnerable to cyberattacks or malicious compromises. Thus this paper presents a novel System-Wide Vulnerability Assessment (SWVA) framework based on Machine Learning, that can be frequently activated to assess the vulnerability of interconnected software components over edge systems. The performance of the SWVA framework is illustrated by assessing the vulnerability of 13 versions of a real world 11 component software system, and comparingthe ARNN results against the well-known ML models MLP, KNN, and Lasso. The results show the superior performance of SWVA, offering over 85% median accuracy and good scalability as the number of connected software components increases.
02038nas a2200145 4500000000100000000000100001008004100002260001200043100001700055700002400072245005300096856007600149490000700225520166000232 2025 d c03/2025 1 aErol Gelenbe 1 aMohammed Nasereddin 00 aAdaptive attack mitigation for IoV Flood Attacks u https://drive.google.com/file/d/1UPsgRyMDKUv4sT6oiOQQXxDgQ8bFTXQM/view 0 v12 3 aGateway Servers for the Internet of Vehicles (IoV) must meet stringent Security and Quality of Service (QoS) requirements, including cyberattack protection, low delays and minimal packet loss, to offer secure real-time data exchange for human and vehicle safety and efficient road traffic management. Therefore, it is vital to protect these systems from cyberattacks with adequate Attack Detection (AD) and Mitigation mechanisms. Such attacks often include packet Floods that impair the QoS of the networks and Gateways and even impede the Gateways’ capability to carry out AD. Thus, this paper first evaluates these effects using system measurements during Flood attacks. It then demonstrates how a Smart Quasi-Deterministic Policy Forwarder (SQF) at the entrance of the Gateway can regulate the incoming traffic to ensure that the Gateway supports
the AD to operate promptly during an attack. Since Flood attacks create substantial packet backlogs, we propose a novel Adaptive Attack Mitigation (AAM) system that is activated after an attack is detected to dynamically sample the incoming packet stream, determine whether the attack is continuing, and also drop batches of packets at the input to reduce the effects of the attack. The AAM is designed to minimize a cost function that includes the sampling overhead and the cost of lost benign packets. We show experimentally that the Optimum AAM approach is effective in mitigating attacks and present theoretical and experimental results that validate the proposed approach.
Federated Learning (FL) is a distributed machine learning framework that enables the collaborative training of machine learning models by multiple entities. However, FL is vulnerable to various potential risks, especially backdoor attacks. A backdoor attack aims to implant hidden backdoors into a global model by compromising one or more clients and making them provide poisoned model updates. Consequently, the global model misclassifies inputs with triggers as adversary-desired classes/labels while performing well on benign inputs. Despite its severity, existing literature lacks a comprehensive review on backdoor attacks and their defense mechanisms of FL, especially for vertical FL. This paper comprehensively reviews and evaluates recent advances in backdoor attacks and defense mechanisms on FL. We first introduce foundational concepts about FL, backdoor attacks, and defense mechanisms, along with their respective security models. Then, we propose two sets of evaluation criteria that a sound backdoor attack and a defense mechanism should meet, respectively. After that, we provide taxonomies of existing backdoor attacks and defense mechanisms of FL and review them by employing the proposed criteria to evaluate their pros and cons. We also explore a positive application of backdoors in FL, i.e., backdoor-based watermarking. Finally, we discuss a number of open issues and suggest promising future research directions.
01778nas a2200169 4500000000100000000000100001008004100002260001200043100001500055700001300070700001400083700001700097245009800114856005100212520133100263022001401594 2025 d c03/2025 1 aTingxi Luo 1 aJie Wang 1 aZheng Yan 1 aErol Gelenbe 00 aGraph Neural Networks for Trust Evaluation: Criteria, State-of-the-Art, and Future Directions uhttps://ieeexplore.ieee.org/document/10925363 3 aThe process of quantifying trust considers the factors that affect it, which can be applied to identify malicious behavior, reduce uncertainty, and facilitate decision-making. Traditional trust evaluation methods based on statistics and reasoning, rely heavily on domain knowledge, which limits their practical applications. Graph Neural Networks (GNNs) are a new Machine Learning (ML) paradigm that can revolutionize the evaluation of trust, by modeling relationships as graphs to simplify relevant data and automating end-to-end evaluation. Thus, a variety of GNN-based trust evaluation models have been developed for different applications. However, there is still a gap in the literature regarding a review on these advances with discussion about remaining challenges. To bridge this gap, we conduct the first review on GNN-based trust evaluation. We first propose a set of criteria in terms of trust-related attributes, correctness, functionality, and overhead. Then, we propose a taxonomy of existing GNN-based trust evaluation models, followed by a review using the proposed criteria to analyze their pros and cons. A quantitative analysis of the recent cutting-edge models is also provided. Based on the review and experimental results, we identify key challenges and suggest future research directions.
a0890-8044 01745nas a2200133 4500000000100000000000100001008004100002100001700043700001700060700001400077700001700091245009000108520141300198 2025 d 1 aZitong Zhang 1 aHaoxiang Han 1 aZheng Yan 1 aErol Gelenbe 00 aA Survey on Cross-Shard Transaction Allocation Optimization and Processing Mechanisms 3 aSharding is one of the most effective technologies for improving blockchain scalability by dividing a blockchain network into multiple shards that process transactions in parallel. However, many existing sharded blockchain systems can include a large number of cross-shard transactions, resulting in high communication overhead and processing latency. Two solutions have emerged to address these issues. One of them focuses on optimizing the allocation of transactions to reduce the cross-shard communication while ensuring workload balance. The other emphasizes the design of an efficient cross-shard transaction processing mechanism to reduce the latency caused by confirming cross-shard transactions. However, the literature still lacks a comprehensive review on the advances of Cross-shard Transaction Allocation Optimization and Processing (CTAOP) mechanisms, which would be a valuable addition to the literature. This paper is a comprehensive survey of existing CTAOP mechanisms, and first introduces cross-shard transactions and related concepts. Then, we propose two sets of criteria for evaluating the two types of CTAOP mechanisms, and provide a taxonomy of CTAOP mechanisms, followed by a thorough review based on our proposed criteria. A list of open issues is also highlighted, and corresponding future research directions are suggested to advance our understanding of CTAOP mechanisms.
02085nas a2200133 4500000000100000000000100001008004100002100001900043700002400062700001700086700002000103245013100123520169700254 2025 d 1 aGodlove Kuaban 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aPiotr Czekalski 00 aTime-Dependent Analysis of the Interaction Between Energy Harvesting, Leakage, and Consumption Processes in a Green IoT Device 3 aThe Internet of Things (IoT) is transforming industries by enhancing productivity and efficiency; however, energy availability remains a significant challenge due to the limited capacity of batteries and supercapacitors powering IoT devices. The emergence of Green IoT (G-IoT) frameworks, which prioritize energy efficiency and renewable energy integration, offers a promising solution to address this challenge. Despite these advancements, energy storage systems (ESSs) face issues such as capacity degradation, leakage, and charge redistribution, which can lead to energy depletion and service disruptions. Traditional models often assume constant energy harvesting rates, overlooking the time-varying nature of environmental conditions that influence energy availability. In this paper, we propose a novel mathematical framework that incorporates time-dependent fluctuations in the energy harvesting rate to analyse the dynamic interactions between energy harvesting, leakage, and consumption in green IoT systems. Our approach includes an Energy Packet Network (EPN) model to represent transient energy dynamics and a Markov model to capture fluctuations in the energy harvesting rate. Through numerical simulations, we evaluate the impact of key design parameters, such as ESS capacity, mean energy consumption rate, energy leakage, and energy harvesting rate variations, on critical performance metrics including the probability of energy depletion and the transient mean number of stored energy packets. The results highlight the importance of considering time-varying energy harvesting in the design and optimization of IoT systems for long-term operation and sustainability.
02212nas a2200157 4500000000100000000000100001008004100002260003300043100001900076700002400095700001700119700001600136700002000152245011000172520177200282 2025 d c12/2025 bIEEE aParis, France 1 aGodlove Kuaban 1 aTadeusz Czachórski 1 aErol Gelenbe 1 aPiotr Pecka 1 aPiotr Czekalski 00 aEnergy Dynamics of Green IoT Nodes with Time-Varying Energy Harvesting, Leakage, and Consumption Patterns 3 aThe growing proliferation of Internet of Things (IoT) devices has intensified the need for sustainable energy solutions, particularly in resource-constrained deployments where non-rechargeable batteries and supercapacitors are the primary energy sources. Green IoT (G-IoT) frameworks address this challenge by combining energy-saving techniques with energy harvesting from ambient sources such as solar power. However, the intermittent nature of renewable energy and the non-ideal behavior of energy storage systems—such as energy leakage and capacity degradation—complicate reliable energy provisioning. This paper presents a novel Markovian framework for modelling the coupled dynamics of time-varying solar energy harvesting, time-dependent energy consumption, and state-dependent energy leakage in G-IoT systems. Unlike traditional steady-state models, our approach uses Discrete-Time Markov Chains (DTMCs) to capture the stochastic variability in both energy harvesting and consumption processes. We also introduce a refined leakage model in which the leakage rate is dynamically dependent on the stored energy level, enabling a more realistic characterization of energy losses due to energy leakage. Through extensive analytical evaluation, we examine how key parameters—such as storage capacity, leakage rate coefficient, and energy harvesting and consumption patterns—affect critical performance metrics, including the mean stored energy and energy-related service outage probability. Furthermore, we propose a parameter tuning strategy to optimize energy reliability and storage efficiency. The proposed model provides valuable insights for the design and optimization of robust, energy-aware IoT systems powered by renewable energy sources.
01666nas a2200133 4500000000100000000000100001008004100002260003900043100002400082700001700106245005900123856004100182520130900223 d bIEEE aVilamoura, Algarve, Portugal 1 aMohammed Nasereddin 1 aErol Gelenbe 00 aParameter Sensitivity of AADRNN-Based Attack Detection uhttps://ieee-iscc.computer.org/2026/ 3 aThe growing prevalence of cyberattacks against network-connected systems increases the need for accurate intrusion and attack detection (AD) systems that can react rapidly at the network access ports. Machine learning has often been used for AD because of the high accuracy it can offer, and its fast testing speed for real-time attack detection. On the other hand, the data sampling methods used to transform incoming traffic streams into data for real-time testing by AD systems can have an important effect on the detection accuracy, and are seldom discussed in the literature. Here, we address this often neglected aspect, with the Auto-Associative Dense Random Neural Network (AADRNN), a state-of-the-art AD system. After briefly reviewing the literature and comparing the AADRNN attack detector with other state-of-the-art approaches, this paper focuses on choosing the values of the data sampling parameters and the decision threshold to obtain the best accuracy from this AD method. Experiments regarding Botnet attack detection using the Mirai dataset, as well as synthetic flood attacks conducted on an experimental IoT test-bed are used to illustrate the results, and to guide the parameter choices that maximize the AADRNN’s True Positive Rate and minimize its False Positive Rate.
01980nas a2200133 4500000000100000000000100001008004100002260002200043100001700065700001100082700001100093245010000104520164200204 d c2025 bIEEE aParis 1 aErol Gelenbe 1 aQ. Ren 1 aZ. Yan 00 aA Probabilistic Dynamic Network Trust Model for IoT Systems with Lost Messages and Cyberattacks 3 atrust model, the Random Neural Network Trust Model (RNNTM), which incorporates the dynamics of trust formation in a network through a sequence of “votes” from each entity regarding all other entities. The model assures fairness among entities through a fixed replenishment rate of “voting rights” for each entity, whose voting rights are reduced each time the entity votes. A positive vote received by an entity increases
its voting rights and its trustworthiness, while a negative vote reduces its voting rights and also its trustworthiness, and a non-negative integer represents each entity’s instantaneous “trust value”. An important property of the RNNTM is that an entity
cannot express its trust or distrust of the other entities, and hence affect their trust values, when its trust level is down to zero, so that untrustworthy entities are not allowed to express trust or distrust. After developing the theoretical characteristics
for the RNNTM model, this paper details its use to evaluate the trust value of multiple entities in a network of Internet of Things (IoT) devices and gateways, where cyberattacks against the gateways, and messages that should be received from IoT devices at regular intervals, modify the parameters that express the trust or distrust between entities. To illustrate its use for a network of interaction gateways, servers and user equipment, several detailed time-dependent simulations of the RNNTM are conducted in the presence of cyberattacks.
Signalling storms have occurred in several generations
of mobile networks due to malevolent end users, as well as
a consequence of service disruptions and system failures. Due to
the massive increase in the volume of traffic and the diversity of
end users and applications in 6G, including the massive access of
the Internet of Things, signalling storms and other cyberattacks
will require attention with research on adequate detection and
mitigation schemes, as we move towards the next generation of
mobile networks. Thus, in the present paper, we conduct discrete
event simulations of a signalling storm, measure its effects in a
simulated environment, and observe that it results in deviations
from the well-known and naturally observed statistical property
known as “Taylor’s Law”. We then discuss a mathematical model
of storms, linking the fraction of malevolent or malfunctioning
users to the rate of call establishment requests, and show that
the mathematical model also exhibits a violation of Taylor’s
Law. Since the detection of cyberattacks from network behaviour
parameters is a challenging problem, we suggest that the violation
of Taylor’s Law may be a useful tool for designing detectors for
signalling attacks, or other network attacks or malfunctions.
Index Terms—Attack Detection and Mitigation, 6G, Signalling
Attacks, Taylor’s Law
Gateways to the Internet of Things (IoT) are typically servers that communicate with IoT devices, providing them with low-latency services, and connecting them to the internet and other backbone networks. Since IoT devices are often simple and have limited storage and computational capabilities, gateways can be equipped with Attack Detection (AD) software to analyze incoming traffic, detect potential cyberattacks, and protect both the gateway and connected devices from threats that could overwhelm the system as a whole. This paper presents an enhanced gateway system that combines a traffic shaping technique with an attack detection module and an optimum attack mitigation scheme aimed at protecting the gateway and the overall system from cyberattacks. The optimum mitigation approach selects a sampling interval for the AD, that minimizes the total overhead of AD and mitigation. The proposed approach is implemented in a practical test-bed, so that the performance of the mitigation scheme may be evaluated in the presence of flood attacks. The experiments show its practical value and illustrate the agreement obtained between the analysis and the measurements obtained from several experiments.
02133nas a2200157 4500000000100000000000100001008004100002260001200043100002000055700001700075700002400092700001600116245010500132490000800237520173000245 2025 d c11/2025 1 aJacob Bergquist 1 aErol Gelenbe 1 aMohammed Nasereddin 1 aKarl Sigman 00 aMitigating massive access with Quasi-Deterministic Transmission: Experiments and stationary analysis 0 v170 3 aThe Massive Access Problem arises due to devices that forward packets simultaneously to servers in rapid succession, or by malevolent software in devices that flood network nodes with high-intensity traffic. To protect servers from such events, attack detection (AD) software is installed on servers, and the Quasi-Deterministic Transmission Policy (QDTP) has been proposed to “shape traffic” and protect servers, allowing attack detection to proceed in a timely fashion by delaying some of the incoming packets individually based on their arrival times. QDTP does not cause packet loss, and can be designed so that it does not increase end-to-end packet delay. Starting with measurements taken on an experimental test-bed where the QDPT algorithm is installed on a dedicated processor, which precedes the server itself, we show that QDPT protects the server from attacks by accumulating arriving packets at the input of the QDTP processor, then forwarding them at regular intervals to the server. We compare the behaviour of the server, with and without the use of QDTP, showing the improvement it achieves, provided that its “delay” parameter is correctly selected. We analyze the sample paths associated with QDTP and prove that when its delay parameter is chosen in a specific manner, the end-to-end delay of each packet remains unchanged as compared to an ordinary First-In-First-Out system. An approach based on stationary ergodic processes is developed for the stability conditions. Assuming mutually independent and identically distributed inter-arrival times, service times and QDTP delays, we exhibit the positive recurrent structure of a two-dimensional Markov process and its regeneration points.
01576nas a2200121 4500000000100000000000100001008004100002260003400043100001500077700001700092245008700109520125800196 2024 d c12/2024 bIEEE aKrakow, Poland 1 aMert Nakip 1 aErol Gelenbe 00 aAn Associated Random Neural Network Detects Intrusions and Estimates Attack Graphs 3 aCyberattacks, especially Botnet Distributed Denial of Service (DDoS), increasingly target networked systems, compromise interconnected nodes by constantly spreading malware. In order to prevent these attacks in their early stages, which includes stopping the spread of malware, it is vital to identify compromised nodes and successfully predict potential attack paths. To this end, this paper proposes a novel system based on an Associated Random Neural Network (ARNN) that simultaneously detects intrusion at the network-level and estimates the network attack graph. In this system, ARNN is trained online to minimize problem-specific multi-task loss so that it identifies compromised network nodes, while the neural network connection weights also estimate the attack path. The performance of the method is calculated using the Kitsune attack dataset, showing that the method achieves a recall rate above 0.95 in estimating the network attack graph, and provides a near-perfect classification of compromised nodes. The ARNN-based system for dynamic and continuous estimation of compromised nodes and network attack graphs, can pave the way for enhancing security measures, and stopping Botnet DDoS attacks from spreading in networked systems.
01691nas a2200121 4500000000100000008004100001260000900042100001700051700001500068700001500083245005300098520141800151 2026 d bIEEE 1 aErol Gelenbe 1 aBaran Gül 1 aMert Nakip 00 aFederated Intrusion Detection for Smart Vehicles 3 aCybersecurity threats endanger networked systems consisting of multiple nodes, such as the Internet of Things (IoT), Supply Chains and Smart Cars, due to the emergence of new types of unknown (zero-day) attacks against vulnerable IoT devices. While earlier research has shown that Machine Learning (ML)-based Intrusion Detection Systems (IDSs) enhance security, the efficacy and practicality of ML-based IDSs often require large training datasets based on private data. This challenge becomes more pronounced in the case of a distributed system, where each node must ensure the confidentiality of local data, yet faces attacks that are similar to those faced by other nodes in the system. Thus, this paper proposes a novel Decentralized Asynchronous IDS with Online Self-Supervised Federated Learning called the iDAF system, that improves overall security by providing collaborative confidentiality-preserving learning among all nodes. iDAF enables self-supervised learning without human intervention or large labelled data, and avoids time-consuming local training sessions. iDAF’s performance is evaluated for DoS and DDoS attacks in two different scenarios of Collaborating IoT Networks and Connected Smart Cars using three online datasets, and compared with five benchmark methods. The results show high intrusion detection accuracy with an acceptable computation time and low communication overhead.