Non-cooperative and cooperative PUEA detection using physical layer in mobile OFDM-based cognitive radio networks

This work proposes novel non-cooperative and cooperative detection methods for identifying primary user emulation attacks (PUEAs) based on a channel-tap power in mobile OFDM-based CR networks. The channel-tap power is utilized as a radio-frequency fingerprint (RFF) to directly detect users via physical (PHY) layer. A channel-based detection for the noncooperative detection is proposed using the Neyman-Pearson test to discriminate between the primary users (PUs) and PUEAs. To improve the detection performance in the shadowing and fading environment, the cooperative detection scheme using the fixed sample size test (FSST) is devised. The proposed methods helps PHY layer completely detect the identities of PUs and PUEAs. From simulation results, for a mobile CR speed of 70 km/h, SNR=-5 dB, and false alarm probability of 0.03, the FSST using ten cooperative nodes can achieve the detection probability of 0.99, which is increased by 1.94 times that of a single node.