An Enhanced Lightweight Trust Framework for Malicious Node Detection in Edge-Enabled IIoT Systems Networks

Abstract

The Industrial Internet of Things (IIoT) is a technology that allows efficient communication between intercon­nected devices, sensors, and edge nodes, making advanced industrial automation possible. But edge devices with limited resources are prone to security issues like malicious node behavior, fake recommendations, and attacks on the trust mechanisms. The existing lightweight trust management schemes are mainly based on direct interaction and static trust assessment, which is not suitable for dynamic environments. In this paper, a new lightweight trust manage­ment system for enhancing the trustworthiness of IIoT networks is proposed. The proposed approach is a hy­brid trust evaluation approach combining direct trust evaluation based on observation and indirect trust evalua­tion based on recommendation feedback. In addition, for achieving continuous adaptation of trust values based on nodes' behaviors, a dynamic trust adaptation strategy is introduced. The results of the simulations under various net­work conditions and malicious node densities demonstrate that the proposed framework offers an effective improve­ment of the accuracy of the Trust Assessment, malicious node detection and defense against dishonest recommen­dations. The model is stable with as many as 50% malicious nodes in the network and has low computa­tional and communication overhead. The framework offers a trustworthy edge computing trust management solu­tion that is secure, scalable and efficient for IIoT environments.