The Detection Scheme Against Selective Forwarding of Smart Malicious Nodes With Reinforcement Learning in Wireless Sensor Networks

Abstract

Wireless sensor networks (WSNs) are extremely vulnerable to different attacks because of open communication, and distribution in unattended areas. The selective forwarding attack is one of the most difficult inside attacks to be detected for two reasons. The node in a harsh environment has to drop some data packets, and the smart malicious node frequently eludes detection. In this paper, we model a selective forwarding attack of smart malicious nodes with a reinforcement learning (RL) algorithm. To effectively detect the selective forwarding attack under a harsh environment, we design the double-threshold density peaks clustering (DT-DPC) algorithm. Abnormal nodes are identified as malicious and isolated owing to continuous abnormalities. Suspicious nodes are determined by the neighbor voting method because malicious behaviors show up separately and a harsh environment universally disturbs agglomerate nodes. Even if smart malicious nodes elude the detection by an RL algorithm, DT-DPC improves the network throughput. The simulation results show that DT-DPC has a low false detection rate (FDR) of around 1% and a missed detection rate (MDR) of around 10%. The network throughput increases by about 4% under a harsh environment.