Clone Attack Detection In Iot Networks Using Reinforcement Learning

Abstract

Clone attacks pose a significant threat to the security and integrity of Internet of Things (IoT) networks. The distributed and often resource-constrained nature of IoT devices makes traditional, centralized security solutions impractical. This study investigates the application of Reinforcement Learning (RL) as a decentralized and adaptive mechanism for detecting clone attacks. The state of the RL environment is defined by the network metrics Received Signal Strength Indicator (RSSI), Packet delivery rate (PDR), node degree and the location coordinates. . Experimental results have shown that the proposed RL model for clone attack detection attains higher accuracy and lesser false positive rate, when compared to the ANN and SVM models.