Sharifi, A. (2019). An Effective and Optimal Fusion Rule in the Presence of Probabilistic Spectrum Sensing Data Falsification Attack. Journal of Communication Engineering, 8(1), 82-97. doi: 10.22070/jce.2019.3577.1101
Abbas Ali Sharifi. "An Effective and Optimal Fusion Rule in the Presence of Probabilistic Spectrum Sensing Data Falsification Attack". Journal of Communication Engineering, 8, 1, 2019, 82-97. doi: 10.22070/jce.2019.3577.1101
Sharifi, A. (2019). 'An Effective and Optimal Fusion Rule in the Presence of Probabilistic Spectrum Sensing Data Falsification Attack', Journal of Communication Engineering, 8(1), pp. 82-97. doi: 10.22070/jce.2019.3577.1101
Sharifi, A. An Effective and Optimal Fusion Rule in the Presence of Probabilistic Spectrum Sensing Data Falsification Attack. Journal of Communication Engineering, 2019; 8(1): 82-97. doi: 10.22070/jce.2019.3577.1101
An Effective and Optimal Fusion Rule in the Presence of Probabilistic Spectrum Sensing Data Falsification Attack
Department of Electrical Engineering, University of Bonab, Bonab, Iran
Abstract
Cognitive radio (CR) network is an excellent solution to the spectrum scarcity problem. Cooperative spectrum sensing (CSS) has been widely used to precisely detect of primary user (PU) signals. The trustworthiness of the CSS is vulnerable to spectrum sensing data falsification (SSDF) attack. In an SSDF attack, some malicious users intentionally report wrong sensing results to cheat the fusion center (FC) and disturb the FC’s global decision on the PU activity. In this paper, we introduce an effective data fusion rule called attack-aware optimal voting rule (AOVR) to confront the SSDF attack in the CSS procedure. In the beginning stages of the cooperative sensing, two important SSDF attack parameters are estimated and then applied in a conventional voting rule to acquire an optimal number of CR users to minimize the global error probability. Two estimated attack parameters include the probabilities of attack in both occupied and empty frequency bands. Simulation results confirm that the proposed attack-aware approach achieves very good performance over the existing conventional cooperative sensing methods.
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