Weighted Distributed Cooperative Spectrum Sensing Using Attack Characteristics

Cooperative spectrum sensing (CSS) improves the reliability of spectrum sensing, however, it is sensitive to the spectrum sensing data falsification (SSDF) attacks. In this paper, a new weighted distributed CSS algorithm, Weighted Distributed Cooperative Spectrum Sensing Using Attack Characteristics (ACWCSS), is proposed. For the proposed algorithm, the attack characteristics of SSDF are employed to design rules for deciding whether the value of the neighbor node is a malicious one or not. Furthermore, the differences between the value of neighbors and the average value in the neighborhood are used to set weight. Numerical simulations show that the proposed algorithm outperforms the Gradient-based Distributed Cooperative Spectrum Sensing (GCSS) and the Largest Deviation-based Distributed Cooperative Spectrum Sensing (LDCSS) which eliminate the neighboring node with the largest deviation value from mean value on both convergence rate and robustness, which causes the significant improvement in detection performance.