<?xml version="1.0" encoding="UTF-8"?>
<xml><records><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Erol Gelenbe</style></author><author><style face="normal" font="default" size="100%">Q. Ren</style></author><author><style face="normal" font="default" size="100%">Z. Yan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Probabilistic Dynamic Network Trust Model for IoT Systems with Lost Messages and Cyberattacks</style></title></titles><keywords/><dates><pub-dates><date><style face="normal" font="default" size="100%">2025</style></date></pub-dates></dates><secondary-title><style face="normal" font="default" size="100%">33rd International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, Paris, France</style></secondary-title><abstract><style face="normal" font="default" size="100%">&lt;p&gt;trust model, the Random Neural Network Trust Model (RNNTM),&#160;which incorporates the dynamics of trust formation in&#160;a network through a sequence of “votes” from each entity&#160;regarding all other entities. The model assures fairness among&#160;entities through a fixed replenishment rate of “voting rights”&#160;for each entity, whose voting rights are reduced each time the&#160;entity votes. A positive vote received by an entity increases&lt;br /&gt;&#13;
its voting rights and its trustworthiness, while a negative vote&#160;reduces its voting rights and also its trustworthiness, and a&#160;non-negative integer represents each entity’s instantaneous “trust&#160;value”. An important property of the RNNTM is that an entity&lt;br /&gt;&#13;
cannot express its trust or distrust of the other entities, and&#160;hence affect their trust values, when its trust level is down to&#160;zero, so that untrustworthy entities are not allowed to express&#160;trust or distrust. After developing the theoretical characteristics&lt;br /&gt;&#13;
for the RNNTM model, this paper details its use to evaluate&#160;the trust value of multiple entities in a network of Internet of&#160;Things (IoT) devices and gateways, where&#160;cyberattacks against&#160;the gateways, and messages that should be received from IoT&#160;devices at regular intervals, modify the parameters that express&#160;the trust or distrust between entities. To illustrate its use for&#160;a network of interaction gateways, servers and user equipment,&#160;several detailed time-dependent simulations of the RNNTM are&#160;conducted in the presence of cyberattacks.&#160;&lt;/p&gt;&#13;
</style></abstract><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Paris</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>
