PETIT: PUF-enabled trust evaluation framework for IoT networks

Abstract

Internet-of-Things (IoT) is characterized by the incorporation of resource constrained devices that are inter-networked in an ad-hoc manner. Given the diversity of the devices and the operating conditions, it is important to assess the trustworthiness of IoT nodes and factor it in the network management. Contemporary trust evaluation and management schemes found in the literature mostly consider observable network-level behavior parameters and initially assume that all nodes are equally trustworthy owing to the absence of historical data or background. Such an equal trust initialization approach raises concerns in terms of accuracy, fairness, and adaptability. This paper aims to mitigate these shortcomings by proposing a novel trust evaluation and aggregation framework. Our framework leverages hardware primitives such as Physical Unclonable Functions (PUFs) to assign trust scores at the network bootstrapping phase. The paper explores the establishment of both direct and recommendation based indirect trust score evaluation and detection of irregularities to ensure the dynamic, safe, and reliable operation of the network. Simulation outcomes demonstrate that the trust value computed through this mechanism effectively and precisely mirrors the node’s credibility.