Anomaly Detection of Wireless Relays Based on Markov Models Through the Wald-Wolfowitz Runs Test

Huangfu, Yingying; Zhou, Liang

doi:10.1109/lcomm.2022.3203041

Cited by 1 publication

(1 citation statement)

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“…ML can provide the desired detection based on massive training data that are inclusive and of satisfactory quality, which, however, is a challenging task to construct the data set of abnormal forwarding behaviors [15], [16]. Recently, researchers have turned to SBS, which provides theoretical foundations for abnormal detection without presuming trust values or depending on training data [17], [21], [22]. However, previous SBS still faces the challenge of requiring massive forwarding observations, leading to expensive costs in low-resource cluster-tree networks [23], [24].…”

Section: A Motivation and Problem Statementmentioning

confidence: 99%

A New Abnormal Node Detection Method With Dynamic Hoeffding Test

Huangfu

Zhou

Zhang

2022

IEEE Wireless Commun. Lett.

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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 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 or frame its normal parent as an anomalous node. To resolve this issue, a modified Z-score-based falsification-resistant mechanism is presented in the CSD to remove untrustworthy information. Through theoretical analysis, we show that the LRTbased method achieves perfect detection, i.e., both the false alarm and missed detection probabilities decay exponentially to zero. Furthermore, the optimal removal threshold of the modified Zscore method is derived for falsifications with uncertain strategies and guarantees perfect detection of the CSD. As our simulation results show, the CSD approach is robust to falsifications and can rapidly reach 99% detection accuracy, even in existing adversarial scenarios, which outperforms state-of-the-art technology.

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