Targeted Context-Based Attacks on Trust Management Systems in IoT
Cody Lewis, Nan Li, Vijay Varadharajan
Abstract
Trust management systems (TMSs) play an important role in Internet of Things (IoT) by providing a means of finding whether a given device can provide a service to a satisfactory level, and for identifying potentially malicious devices in the network. Context awareness extends trust models by allowing a trustor to filter and aggregate evidence by their relevance to the current situation. Context awareness is important in the formulation of trust in IoT networks due to their heterogeneity and due to the dynamic changes in the capabilities of IoT devices. In this article, we have proposed a new type of attack on context-aware trust models for IoT systems, context-based attacks. In this attack, an adversary manipulates the context to impact a target group of IoT devices, while other devices in nontargeted groups are not even aware of the attack. We have demonstrated the effectiveness of this new type of attack on seven previously proposed trust models through practical simulations and theoretical proofs. This article also proposes a new TMS that can mitigate such context-based attacks.