Physical-layer intrusion detection for wireless networks using compressed sensing

The broadcast nature of wireless networks has been widely exploited by adversaries in order to cause severe denial-of-service attacks. Several algorithms are proposed in the literature for the detection and mitigation of such attacks at the physical and medium access layers. In this work, we combine recent advances in compressed sensing theory, along with a cumulative-sum anomaly-based algorithm, for the detection of physical-layer attacks. The algorithm considers a metric based on the Signal-to-Interference-plus-Noise-Ratio (SINR). Compressed sensing makes feasible the use of far fewer SINR measurements for effective intrusion detection. The performance evaluation based on real experimental data shows that attacks are detected with high accuracy using a small number of measurements.