Distributed Quantized Detection of Sparse Signals Under Byzantine Attacks

This paper investigates distributed detection of sparse stochastic signals with quantized measurements under Byzantine attacks, where sensors may send falsified data to the Fusion Center (FC) to degrade system performance. Here, the Bernoulli-Gaussian (BG) distribution is used to model sparse stochastic signals. Several detectors with significantly improved detection performance are proposed by incorporating estimates of attack parameters into the detection process. In the case of unknown sparsity degree and attack parameters, we propose the generalized likelihood ratio test with reference sensors (GLRTRS) as well as the locally most powerful test with reference sensors (LMPTRS). Our simulation results show that these detectors outperform the LMPT and GLRT detectors designed in attack-free environments and achieve detection performance close to the benchmark likelihood ratio test (LRT) detector. In the case of unknown sparsity degree and known fraction of Byzantine nodes in the network, we further propose enhanced LMPTRS (E-LMPTRS) and enhanced GLRTRS (E-GLRTRS) detectors by filtering out potential malicious sensors in the network, resulting in improved detection performance compared to GLRTRS and LMPTRS detectors.