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<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%">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/><AUTHORS><style face="normal" font="default" size="100%"/></AUTHORS><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><PLACE_PUBLISHED><style face="normal" font="default" size="100%">Paris</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%">2025</style></DATE><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><title><style face="normal" font="default" size="100%">A Probabilistic Dynamic Network Trust Model for IoT Systems with Lost Messages and Cyberattacks</style></title></RECORD></records></xml>
