Distributed event detection under Byzantine attack in wireless sensor networks

We present two novel distributed event detection algorithms based on a statistical approach that tolerate Byzantine attacks where malicious (compromised) sensors send false sensing data to the gateway leading to increased false alarm rate. We study the problem of Byzantine attack function optimization and the decision threshold optimization and consider two practical cases in our algorithms. In the first case, the Channel State Information (CSI) between the event generating source and sensors is unknown while CSI between the sensors and gateway is known. In the second case, the CSI between the source and sensors as well as between sensors and gateway are unknown. We develop an optimal event detection decision rule under Byzantine attacks for the first case and a novel low-complexity event detection algorithm based on Gaussian approximation and Moment Matching for the second case which considers a global decision. We evaluate our algorithms through extensive simulations. Simulation results show the Receiver Operating Characteristics (ROC) curves under different cases and scenarios, and therefore provide useful upper bounds for various centralized and distributed scheme designs. We also show that our algorithms provide superior detection performance when compared to local decision based schemes.