<?xml version="1.0" encoding="UTF-8"?>
<xml><records><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Hamzeh AlKilani</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mohammed Nasereddin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ali Hadi</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Sara Tedmori</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Data Exfiltration Techniques and Data Loss Prevention System</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2019</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">2019 International Arab Conference on Information Technology (ACIT)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Al Ain University of Science and Technology, Al Ain, United Arab Emirates</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">12/2019</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;One of the primary concerns of data security specialists is to mitigate insider threats and prevent data leaks. Often, unfortunately, insider threats go unnoticed. In most cases, the longer such activity goes unnoticed, the greater the resulting damages are likely to be. This paper provides an overview of the basic data exfiltration techniques that deal with file structures and were utilized in multiple scenarios in an attempt to bypass a Data Loss Prevention system. Details show which of the scenarios have been detected and which have not been by the Data Loss Prevention system. The paper also proposed solutions for the undetected scenarios.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/8991131</style></URL><title><style face="normal" font="default" size="100%">Data Exfiltration Techniques and Data Loss Prevention System</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Mohammed Nasereddin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Ashaar ALKhamaiseh</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Malik Qasaimeh</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Raad Al-Qassas</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">A systematic review of detection and prevention techniques of SQL injection attacks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Information Security Journal: A Global Perspective</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><DATE><style face="normal" font="default" size="100%">10/2021</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;SQL injection is a type of database-targeted attack for data-driven applications. It is performed by inserting malicious code in the SQL query to alter and modify its meaning, enabling the attacker to retrieve sensitive data or to access the database. Many techniques have been improved and proposed to detect and mitigate these types of attacks. This paper provides a systematic review for a pool of 60 papers on web applications’ SQL injection detection methods. The pool was selected using a developed searching and filtering methodology for the existing literature based on scholar databases (IEEE, ScienceDirect, and Springer) with the aim to provide specific answering for several research questions in the area of SQL injection detection. This provides a basis for the design and use of effective SQL injection detection methods.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://www.tandfonline.com/doi/abs/10.1080/19393555.2021.1995537</style></URL><title><style face="normal" font="default" size="100%">A systematic review of detection and prevention techniques of SQL injection attacks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Mohammed Nasereddin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mert Nakip</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Measurement Based Evaluation and Mitigation of Flood Attacks on a LAN Test-Bed</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2023</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">The 48th IEEE Conference on Local Computer Networks</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Daytona Beach, Florida, USA</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The 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.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/document/10223358</style></URL><title><style face="normal" font="default" size="100%">Measurement Based Evaluation and Mitigation of Flood Attacks on a LAN Test-Bed</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mohammed Nasereddin</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Protecting IoT Servers Against Flood Attacks with the Quasi Deterministic Transmission Policy</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2023</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">The 22nd IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom-2023)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Exeter, UK</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Servers 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.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/abstract/document/10538961</style></URL><title><style face="normal" font="default" size="100%">Protecting IoT Servers Against Flood Attacks with the Quasi Deterministic Transmission Policy</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Mohammed Nasereddin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Raad Al-Qassas</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">A New Approach for Detecting Process Injection Attacks Using Memory Analysis</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">International Journal of Information Security</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><DATE><style face="normal" font="default" size="100%">03/2024</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;This paper introduces a new approach for examining and analyzing fileless malware artifacts in computer memory. The proposed approach offers the distinct advantage of conducting a comprehensive live analysis of memory without the need for periodic memory dumping. Once a new process arrives, log files are collected by monitoring the Event Tracing forWindows facility as well as listing the executables of the active process for violation detection. The proposed approach significantly reduces detection time and minimizes resource consumption by adopting parallel computing (programming), where the main software (Master) divides the work, organizes the process of searching for artifacts, and distributes tasks to several agents (Slaves). A dataset of 17411 malware samples is used in the assessment of the new approach. It provided satisfactory and reliable results in dealing with at least six different process injection techniques including classic DLL injection, reflective DLL injection, process hollowing, hook injection, registry modifications, and .NET DLL injection. The detection accuracy rate has reached 99.93% with a false-positive rate of 0.068%. Moreover, the accuracy was monitored in the case of launching several malwares using different process injection techniques simultaneously, and the detector was able to detect them efficiently. Also, it achieved a detection time with an average of 0.052 msec per detected malware.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">A New Approach for Detecting Process Injection Attacks Using Memory Analysis</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Mohammed Nasereddin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Abdallah Qusef</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Risk Assessment in Smartphones: Comprehensive Analysis</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2021</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">International Conference on Data Science, E-learning and Information Systems 2021</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Online - Jordan</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">ACM</style></PUBLISHER><DATE><style face="normal" font="default" size="100%">06/2021</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Smartphones are multi-purpose and widely used devices in various fields, in terms of personal use, and companies and governments. Increasing dependence on them opened the way for increased violations on its users, as it became a fat substance for criminals, for their illegal practices, so it encountered serious and targeted security threats. This paper presents a set of rules and controls that will reduce the risks that threaten smartphone users. Furthermore, this work provides the methods of risk assessment that are related to smartphones. It identifies the assets and lists several different threats that are relevant. Each risk has its impact on the assets, this is described by mapping it with the likelihood of each threat.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Risk Assessment in Smartphones: Comprehensive Analysis</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Mohammed Nasereddin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mert Nakip</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Deep Learning Intrusion Detection and Mitigation of DoS Attacks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2024</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">32nd International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS24)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Krakow, Poland</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Internet 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.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/abstract/document/10786560</style></URL><title><style face="normal" font="default" size="100%">Deep Learning Intrusion Detection and Mitigation of DoS Attacks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mohammed Nasereddin</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Adaptive attack mitigation for IoV Flood Attacks</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE  Internet of Things Journal</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">12</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">5</style></ISSUE><SECTION><style face="normal" font="default" size="100%">4701-4714</style></SECTION><DATE><style face="normal" font="default" size="100%">03/2025</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;Gateway Servers for the Internet of Vehicles (IoV)&#160;must meet stringent Security and Quality of Service (QoS)&#160;requirements, including cyberattack protection, low delays and&#160;minimal packet loss, to offer secure real-time data exchange for&#160;human and vehicle safety and efficient road traffic management.&#160;Therefore, it is vital to protect these systems from cyberattacks&#160;with adequate Attack Detection (AD) and Mitigation mechanisms. Such attacks often include packet Floods that impair&#160;the QoS of the networks and Gateways and even impede the&#160;Gateways’ capability to carry out AD. Thus, this paper first&#160;evaluates these effects using system measurements during Flood&#160;attacks. It then demonstrates how a Smart Quasi-Deterministic&#160;Policy Forwarder (SQF) at the entrance of the Gateway can&#160;regulate the incoming traffic to ensure that the Gateway supports&lt;br /&gt;&#13;
the AD to operate promptly during an attack. Since Flood attacks&#160;create substantial packet backlogs, we propose a novel Adaptive&#160;Attack Mitigation (AAM) system that is activated after an attack&#160;is detected to dynamically sample the incoming packet stream,&#160;determine whether the attack is continuing, and also drop batches&#160;of packets at the input to reduce the effects of the attack. The&#160;AAM is designed to minimize a cost function that includes the&#160;sampling overhead and the cost of lost benign packets. We show&#160;experimentally that the Optimum AAM approach is effective&#160;in mitigating attacks and present theoretical and experimental&#160;results that validate the proposed approach.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%"> https://drive.google.com/file/d/1UPsgRyMDKUv4sT6oiOQQXxDgQ8bFTXQM/view</style></URL><title><style face="normal" font="default" size="100%">Adaptive attack mitigation for IoV Flood Attacks</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Mohammed Nasereddin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Parameter Sensitivity of AADRNN-Based Attack Detection</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><SECONDARY_TITLE><style face="normal" font="default" size="100%">31st Symposium on Computers and Communications (IEEE ISCC 2026)</style></SECONDARY_TITLE><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Vilamoura, Algarve, Portugal</style></PLACE_PUBLISHED><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><PUBLISHER><style face="normal" font="default" size="100%">IEEE</style></PUBLISHER><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The 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.&lt;/p&gt;&#13;
</style></ABSTRACT><URL><style face="normal" font="default" size="100%">https://ieee-iscc.computer.org/2026/</style></URL><title><style face="normal" font="default" size="100%">Parameter Sensitivity of AADRNN-Based Attack Detection</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>31</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mohammed Nasereddin</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Data Driven Optimum Cyberattack Mitigation</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">IEEE DSAA'25: Data Science and Advanced Analytics Conference, Birmingham, UK</style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Data Driven Optimum Cyberattack Mitigation</style></title></RECORD><RECORD><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><REFERENCE_TYPE>0</REFERENCE_TYPE><CONTRIBUTORS><AUTHORS><AUTHOR><style face="normal" font="default" size="100%">Jacob Bergquist</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Erol Gelenbe</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Mohammed Nasereddin</style></AUTHOR><AUTHOR><style face="normal" font="default" size="100%">Karl Sigman</style></AUTHOR></AUTHORS></CONTRIBUTORS><TITLES><TITLE><style face="normal" font="default" size="100%">Mitigating massive access with Quasi-Deterministic Transmission: Experiments and stationary analysis</style></TITLE></TITLES><KEYWORDS/><DATES/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><YEAR><style face="normal" font="default" size="100%">2025</style></YEAR><SECONDARY_TITLE><style face="normal" font="default" size="100%">Performance Evaluation </style></SECONDARY_TITLE><LANGUAGE><style face="normal" font="default" size="100%">eng</style></LANGUAGE><VOLUME><style face="normal" font="default" size="100%">170</style></VOLUME><ISSUE><style face="normal" font="default" size="100%">102512</style></ISSUE><SECTION><style face="normal" font="default" size="100%">1 - 12</style></SECTION><DATE><style face="normal" font="default" size="100%">11/2025</style></DATE><ABSTRACT><style face="normal" font="default" size="100%">&lt;p&gt;The 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.&lt;/p&gt;&#13;
</style></ABSTRACT><title><style face="normal" font="default" size="100%">Mitigating massive access with Quasi-Deterministic Transmission: Experiments and stationary analysis</style></title></RECORD></records></xml>
