Detecting selective forwarding attacks in WSN based on deep belief network

Abstract

Wireless Sensor Networks (WSNs) communicate by broadcasting, and are usually deployed in unattended locations, making them vulnerable to attacks. Selective Forwarding (SF) attack is an internal attack and is difficult to detect because of its uncertain packet loss. In order to improve the accuracy and speed of detection SF, this paper uses Deep Belief Network (DBN) to detect compromised nodes. The scheme clusters the nodes and then trains the deep network to get the prediction value of forwarding rate. Based on the prediction error, malicious nodes can be found out. Experimental results show that the scheme can detect all malicious nodes in an ideal condition. In harsh environment, both Miss Detection Rate (MDR) and False Detection Rate (FDR) are around 5% on average. Moreover, it takes a short time to perform a detection, which can reduce the harm of the attack as soon as possible.