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The inherent nature of cognitive radio (CR) networks has brought new threats to wireless communications. Primary user emulation attack (PUEA) has been widely studied as a serious threat to cooperative spectrum sensing (CSS) in CR networks. In PUEA, a malicious user can obstruct CR users from accessing idle frequency bands by imitating licensed primary user (PU) signal characteristics. The present study introduces a new CSS scheme in the presence of a malicious PUEA based on multi-level hypothesis testing (MLHT). In the proposed method, generalizing from binary hypothesis testing to MLHT, we partition the decision space to four decision options and apply minimum Bayes cost criteria to determine the channel status. We also discuss practical limitation issues that need to be considered when applying the MLHT approach. Simulation results are provided to indicate the performance improvement of the proposed MLHT method against PUEA, compared with the conventional method.

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Spectrum sensing is the main function of cognitive radio (CR), which enables the CR users to detect the spectrum holes. Inherent characteristics of CR have imposed some serious threats to the networks. One of the common threats in CR network is primary user emulation attack (PUEA). In this particular type of attack, some malicious users try to imitate primary signal characteristics and defraud CR users to prevent them from accessing the spectrum holes. Therefore, an effective defense strategy is extremely important for robust collaborative spectrum sensing (CSS). The current study introduces a new CSS scheme in the presence of an intelligent PUEA, called attackaware CSS (ACSS), which is aware of spectrum holes and actually co-located with the licensed primary user (PU) and transmits with the same power level in a way that CR users are not easily able to differentiate between received signal from PU and PUEA. The idea is based on attack strength estimation, where the attack strength is defined as the channel occupancy rate of malicious PUEA which equals to the probability that the malicious emulator occupies a specific spectrum hole. The proposed approach estimates the attack strength and innovatively applies in NeymanÀPearson or likelihood ratio test to improve collaborative sensing performance. Simulation results are provided to indicate the superiority of the proposed ACSS method against PUEA compared with the conventional method.