Interaction-based Detection Strategy Against Probabilistic SSDF Attack in CSS Network
Zhixu Cheng, Jing Zhang, Tiecheng Song, Jing Hu, Xu Bao
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This paper investigates the malicious users (MUs) detection problem with M-ary quantized data against spectrum sensing data falsification (SSDF) attack in cooperative spectrum sensing (CSS) network. We consider a CSS network with M-ary quantized data, in which there are interactions among secondary users (SUs). We simplify the interaction process with proper assumptions. For the detection of MUs, we construct a detection architecture with SUs partitioned into groups of two. Then we propose a detection algorithm based on interaction. Simulation results prove the correctness of proposed theorem. Compared with previous detection schemes, proposed scheme shows better detection performance.