利用在线异常感知的无线电网络抗SSDF攻击协作频谱感知方案

该文提出了一种时空频率CSS框架，用于检测和跟踪CR网络中的恶意用户和异常测量。联合频谱感知和恶意用户识别问题构成优化问题，且利用固有稀疏到频谱占用和恶意用户发生。提出的方案利用在线异常感进行实时识别和跟踪，通过节点位置信息获得了改进的性能，可有效处理错过或不正确的位置信息，以及PU功率级噪声SU测量。实验结果表明,提出的方案在协作感知中能够有效克服感知数据错误化(SSDF)攻击。此外，提出的方案优于现有的较为先进的基于PCA方法，具有足够的灵活性来抵御SU网络中的各种攻击。

A cooperative spectrum sensing schema against SSDF attack using online abnormal perception in cognitive radio networks

A framework of a generalized robust cooperative spectrum sensing (CSS) is proposed, in which spectrum sensing and malicious user identification problem are combined an optimization problem. The proposed scheme could improve the performance by utilizing the location information of the nodes, and Online abnormal perception is used to real-timely identification and tracking, it can identify the malicious users and spectrum opportunities and can settle problems such as inaccurate, missing distance measurement and noise SU reports . The experimental results show that, the proposed schema can effectively overcome the SSDF attack.