False Discovery Rate Based Distributed Detection in the Presence of Byzantines

TL;DR

This paper enhances distributed detection in wireless sensor networks by optimizing FDR control with the Kolmogorov-Smirnov metric and introduces an adaptive algorithm to mitigate Byzantine sensor attacks, improving robustness and detection accuracy.

Contribution

It proposes using the Kolmogorov-Smirnov distance for better FDR-based detection and develops an adaptive algorithm to counteract Byzantine sensor falsification.

Findings

FDR control with KS metric improves detection performance.

Detection degrades with increasing Byzantines, but adaptive algorithm mitigates this.

Simulation confirms robustness of the proposed method.

Abstract

Recent literature has shown that the control of False Discovery Rate (FDR) for distributed detection in wireless sensor networks (WSNs) can provide substantial improvement in detection performance over conventional design methodologies. In this paper, we further investigate system design issues in FDR based distributed detection. We demonstrate that improved system design may be achieved by employing the Kolmogorov-Smirnov distance metric instead of the deflection coefficient, as originally proposed in Ray&VarshneyAES11. We also analyze the performance of FDR based distributed detection in the presence of Byzantines. Byzantines are malicious sensors which send falsified information to the Fusion Center (FC) to deteriorate system performance. We provide analytical and simulation results on the global detection probability as a function of the fraction of Byzantines in the network. It is observed that the detection performance degrades considerably when the fraction of Byzantines is large. Hence, we propose an adaptive algorithm at the FC which learns the Byzantines' behavior over time and changes the FDR parameter to overcome the loss in detection performance. Detailed simulation results are provided to demonstrate the robustness of the proposed adaptive algorithm to Byzantine attacks in WSNs.