Detecting multi-channel wireless microphone user emulation attacks in white space with noise

Abstract

Abstract-In this work, we study a special kind of primary user emulation (PUE) attack, named wireless microphone user emulation (WMUE) attack in white space cognitive radio networks. In WMUE attacks, a malicious user emulates wireless microphone (WM) signals in order to block secondary users. Existing work on WMUE attack detection deals with single channel senario. Although multi-channel WM (MCWM) systems are common, detecting WMUE attacks under a multi-channel setting in noisy environments has not been well studied and the existing solution for single channel case cannot be directly applied. In a practical multi-channel WM system, the audio signals on different channels mix with each other and are contaminated by noises, which introduce great challenges on WMUE attack detection. We propose a novel multi-channel WMUE attack detection scheme which is based on the crosscorrelation between the demodulated FM signal and the acoustic signal. The audio interferences, audio noises, and RF noises are all resisted by the cross-correlation. To reduce computation complexity, we propose a 1.5-bit FM demodulator whose outputs are represented by only 0, 1 and -1. Moreover, we set up a MCWM system and developped a hardware based prototype to evaluate the performance of the proposed scheme. Experimental results show that, the proposed scheme can effectively detect multi-channel WMUE attacks within 0.25 second with detection rate larger than 0.9 and false alarm rate lower than 0.1 under low signal-to-noise ratios.

A soft cooperative spectrum sensing in the presence of most destructive smart PUEA using energy detector

“…In the work of Chen and Park, the authors used the distance ratio test (DRT) and the distance different test (DDT) to authenticate the primary users identity. In the work of Shan et al, the authors presented a detection scheme for the wireless microphone user emulation (WMUE) attack (a particular kind of PUE attack), which uses the cross‐correlation between the acoustic signal and demodulated FM signal in the cognitive radio network. In the work of Yu et al, a two‐level database‐assisted detection approach was proposed.…”