Identify Selective Forwarding Attacks Using Danger Model: Promote the Detection Accuracy in Wireless Sensor Networks

Wireless sensor networks (WSNs) are prone to attack due to open work conditions and broadcast communication. The selective forwarding attack, one of the inner attacks in WSNs, is the hardest attack to detect among denial of service (DoS) attacks. The malicious nodes which launch the selective forwarding attack will drop part of or all the data packets they received. Many proposed detection methods against selective forwarding attacks have low accuracy or high algorithm complexity, especially when the attacker works its way in the network along with several attacks, such as blackhole, wormhole, and Distributed Denial of Service (DDoS). Here, an artificial immune system based on the danger model is established to detect network attacks. To promote detection accuracy and reduce computation, we have proposed a screen-confirm scheme. In the screening phase, the scheme first obtains the danger signals from nodes’ energy consumption, forwarding rate, connect duration, transmission frequency, and transmission time, then it screens out the suspected selective forwarding attacks from DoS attacks using a support vector machine (SVM). After that, in the confirming phase, we select an optimal danger threshold and calculate the outputs of the suspected ones, then confirm them by comparing outputs and threshold. The simulation results show that our scheme’s missing detection rate (MDR) is close to 1.3% and keeps the false detection rate (FDR) lower than 4.3% when the malicious ratio is 10%. Moreover, compared with other related work, our proposed method offers a lower algorithm complexity.