Context-awareness trust management model for trustworthy communications in the social Internet of Things

The social Internet of Things (SIoT) is the next generation of the Internet of Things network. It entails the evolution of intelligent devices into social ones, aiming at building interactions with people in order to link groups and develop their own social context. Because a high volume of data is shared throughout the network’s diverse nodes, security measures are essential to ensure that users may interact safely. Trust management (TM) models have been presented in the literature to avoid detrimental interactions and preserve a system’s optimal functioning. In reality, given the SIoT context of nodes varies over time, a TM mechanism must contain methods for evaluating the level of trustworthiness. Existing methods, on the other hand, continue to lack effective solutions for addressing contextual SIoT attributes that define the network node while assessing trust. The utmost objective of this paper is to perform an in-depth analysis of contextual trust-awareness based on the defined TM model “CTM-SIoT” in order to more precisely detect malicious SIoT nodes to maintain safe network connections. As part of our trust evaluation process, machine learning techniques are employed to study the behavior of nodes. Our objective is to limit contacts with aggressive and unskilled service providers. Experimentation was carried out using the Cooja simulator on a simulated SIoT dataset based on real social data. With an F-measure value of up to 1, we validated the Artificial Neural Network’s suitability as a classifier for our issue statement. When compared to other conventional trust classification methods, the findings demonstrated that handling contextual SIoT characteristics inside our TM model enhanced the performance of a TM mechanism with a 0.037% rise in F-measure and a 0.13% drop in FPR, in identifying malicious nodes even for a system with 50% of malicious transactions.