Conference:
33rd International Conference on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS 2025), 21-23. October 2025, Sorbonne University, Paris, FR
Authors:
Gelenbe E, Ren Q, Yan Z.
Abstract:
This paper introduces a new dynamic networked trust 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.
