利用SSDF攻击特征的加权分布式频谱感知算法

协作频谱感知可以提高频谱感知的可靠性，但易遭到篡改感知数据(Spectrum Sensing Data Falsification，SSDF)攻击。该文利用SSDF攻击特征，判断邻居节点发送值是否是恶意状态值，并提出一种加权分布式协作频谱感知算法。该算法根据状态值在本地节点网络中的偏离程度，设定其融合权值。仿真结果表明，所提算法在节点收敛率和鲁棒性两方面，比基于梯度的协作频谱感知算法和基于最大差值的协作频谱感知算法都有所提升，检测性能也因此显著提高。 

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.