Context-oriented trust computation model for industrial Internet of Things

Abstract

The Industrial Internet of Things (IIoT) has revolutionized the industrial sector by providing advanced and intelligent applications. The objects and nodes communicate with one another to collect, exchange, and analyze a large amount of sensing data using techno-social systems, thereby challenging the security and trustworthiness of the data. To achieve effective communication in IIoT, trustworthy relationships must be established among these objects. This makes trust an important security parameter in an IoT-based environment to achieve secure and reliable service communication at the edge nodes. In this paper, we propose an adaptive Context-Based Trust Evaluation System (CTES), which calculates distributed trust at the node level to achieve edge intelligence. Each edge node takes recommendations from its context-similar nodes to calculate the trust of serving nodes. This collaborative trust calculation mechanism helps in filtering out malicious nodes in the network. The weighing factor “μ” is dynamically assigned based on the previously calculated trust score experienced by the edge node. This research also focuses on formal verification of the proposed CTES model. We analyze the efficiency of CTES in terms of accuracy, dynamic assignment of μ, and resiliency against Ballot Stuffing and Bad Mouthing attacks to avoid malicious nodes. The results ensure the significance of the proposed CTES model for dynamic assignment of μ and provide satisfactory results against EigenTrust, ServiceTrust, and ServiceTrust++ in terms of detecting malicious nodes and isolating them from providing recommendations.