Turkish Journal of Electrical Engineering and Computer Sciences




The Internet of Things (IoT) comprises of a diverse network of homogeneous and heterogeneous nodesthat can be accessed through network ubiquitously. In unattended environments, the IoT devices are prone to variousattacks including ballot-stu?ing, bad-mouthing, self-promotion, on-off, opportunistic behavior attacks, etc. The on-offattack is di?icult to detect as nodes switch their behavior from normal to malicious alternatively. A trust managementmodel is a tool to defend the IoT system against malicious activities and provide reliable data exchange. The majorityof existing IoT trust management techniques are based on static reward and punishment values in pursuit of trustcomputation thereby allowing the misbehaving nodes to deliberately perform on-off attacks. Due to the static nature ofawarding scores, these schemes fail to identify malicious nodes in certain cases. In this paper, a dynamic and distributedtrust management scheme (DDTMS) is proposed where nodes can autonomously evaluate peer nodes' behavior anddynamically grant reward and penalty score. The proposed scheme successfully detects the on-off attack and isolates themisbehaving nodes thereby classifying them into three distinct categories based on their severity levels i.e. low, mild,and severe. Simulation-based performance evaluation shows improved performance of the proposed DDTMS againstother state-of-the-art schemes thereby requiring less time and fewer interactions for successfully identifying malevolentbehavior of compromised nodes.

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