宽带SSDF攻击下的压缩频谱感知

在宽带主用户信号的模型下,提出了一种新型的宽带SSDF(spectrum sensing data falsification)攻击模式,并结合了压缩感知(compressed sensing,CS)技术与平均一致(average consensus)算法,建立了可防御这种宽带SSDF攻击的分布式宽带压缩频谱感知模型.频谱感知分为感知阶段和信息融合阶段.在感知阶段,各个CR(cognitiveradio)节点对接收到的主用户信号进行压缩采样以减少对宽带信号采样的开销和复杂度,并作出本地频谱估计.在信息融合阶段,为了更好地排除宽带SSDF攻击节点的影响,建立了子信道声望值指标,各CR节点的本地频谱估计结果以分布式的方式进行信息融合,排除潜在恶意次用户的影响,得到最终的频谱估计结果.仿真结果表明,提出的频谱感知模型可以有效地抵御宽带SSDF攻击,并且利用了恶意次用户节点的有用数据获得了额外的分集增益,提高了频谱感知的性能.

Compressed spectrum sensing defensive against wideband SSDF attacks

This paper proposes a new SSDF attacking model called Wideband SSDF Attack, and on the basis that the wireless signal in cognitive radio network is inherently sparse in frequency domain, this paper develops a distributed compressed wideband spectrum sensing approach which combines compressed sensing and average consensus algorithm to defend against such wideband SSDF attacks. Spectrum sensing takes two stages: sensing stage and fusion stage. At sensing stage, compressed sensing is performed at each CR node to sample the received time domain wideband primary user signal at practical complexity and cost, and then each CR node locally reconstructs the frequency domain signal. At fusion stage, to defend against wideband SSDF attacks, we evaluate reputation values for each subchanel, and then the local spectrum sensing results of each CR are fused and then we can exclude the influence of potential malicious node at the same time without a fusion center at each subchanel. Simulation results show that the proposed spectrum sensing scheme can defend against wideband SSDF attacks, and spectrum sensing performance is enhanced and at the same time we get extra diversity gain by exploiting useful information from malicious node using our proposed model.