Cooperative Abnormal Node Detection with Adversary Resistance

This paper presents a novel probabilistic detection scheme called Cooperative Statistical Detection~(CSD) for abnormal node detection while defending against adversarial attacks in cluster-tree wireless sensor networks. The CSD performs a two-phase process: 1) designing a likelihood ratio test~(LRT) for a non-root node at its children from the perspective of packet loss; 2) making an overall decision at the root node based on the aggregated detection data of the nodes over tree branches. In most adversarial scenarios, malicious children knowing the detection policy can generate falsified data to protect the abnormal parent from being detected. To resolve this issue, a mechanism is presented in the CSD to remove untrustworthy information. Through theoretical analysis, we show that the LRT-based method achieves the perfect detection. Furthermore, the optimal removal threshold is derived for falsifications with uncertain strategies and guarantees perfect detection of the CSD. As our simulation results shown, the CSD approach is robust to falsifications and can rapidly reach $99\%$ detection accuracy, even in existing adversarial scenarios, which outperforms the state-of-the-art technology.