Abstract
Collaborative spectral sensing can fuse the perceived results of multiple cognitive users, and thus will improve the accuracy of perceived results. However, the multi-source features of the perceived results result in security problems in the system. When there is a high probability of a malicious user attack, the traditional algorithm can correctly identify the malicious users. However, when the probability of attack by malicious users is reduced, it is almost impossible to use the traditional algorithm to correctly distinguish between honest users and malicious users, which greatly reduces the perceived performance. To address the problem above, based on the β function and the feedback iteration mathematical method, this paper proposes a malicious user identification algorithm under multi-channel cooperative conditions (β-MIAMC), which involves comprehensively assessing the cognitive user’s performance on multiple sub-channels to identify the malicious user. Simulation results show under the same attack probability, compared with the traditional algorithm, the β-MIAMC algorithm can more accurately identify the malicious users, reducing the false alarm probability of malicious users by more than 20%. When the attack probability is greater than 7%, the proposed algorithm can identify the malicious users with 100% certainty.
Highlights
Spectrum sensing is a key link of cognitive radio and the perceived performance will directly affect the performance of the whole cognitive radio system [1]
Distributed collaborative spectrum sensing technology can rely on the perception results of neighboring cognitive users to achieve an increased sensing accuracy, but the method will greatly increase the complexity of the terminal design [3]
Based on existing research results and the characteristics of collaborative spectrum sensing, we propose using the β-MIAMC algorithm to solve the problem of malicious user identification when the malicious user attack probability is small
Summary
Spectrum sensing is a key link of cognitive radio and the perceived performance will directly affect the performance of the whole cognitive radio system [1]. The centralized collaborative spectrum sensing technology can greatly improve the perceived precision because it can obtain the perceived result of all the cognitive users in the data fusion center. In order to enhance their own concealment, II-MUs usually send tampered perceived results with a smaller probability This behavior is called Spectrum Sensing Data False (SSDF) attacks [4]. Reference [8] proposed a single channel centralized spectrum sensing algorithm based on the β-reputation (β-SCCSA) system [9] This algorithm determines the cognitive user’s reputation and weight by knowing the cognitive user’s historical performance and can react effectively against malicious cognitive users. Based on existing research results and the characteristics of collaborative spectrum sensing, we propose using the β-MIAMC algorithm to solve the problem of malicious user identification when the malicious user attack probability is small. When the attack probability is greater than 7%, the proposed algorithm can distinguish malicious users with 100% certainty
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