In cognitive radio network (CRN), effective spectrum management provides better quality of service. The spectrum is limited but the significance of the spectrum is increasing at each network generation. Due to the ineffective spectrum allocation policies, several researches have indicated that a vast segment of the licensed radio is not viably used. A CRN is an intelligent spectrum utilization innovation that provides better spectrum interface. Spectrum sensing detects unused spectrum in the manner that protects interferences to the authorized users. In principle, the secondary user (SU) receives the primary user (PU) signal and reports it to the fusion center for decision or spectrum allocation. The SU cooperates in the detection of the presence or absence of the PU. This type of spectrum sensing is called cooperative spectrum sensing. However, the significance of this type of spectrum sensing is blurred by the security problems. Malicious users can deliberately report misleading information regarding the presence of the PU. Hence, in this paper, a support vector machine learning algorithm is proposed to statistically learn the behavior of the malicious users and it classifies the legitimate SU and malicious users. A particle swarm optimization algorithm is also integrated to learn the smallest possible distinguishable malicious users’ energy report deviation from the legitimate SUs. The probability of detection and energy of detection have been applied to evaluate the contribution of the proposed method. Finally, the simulation results have confirmed that better spectrum management can be derived from the proposed statistical approach.

在认知无线电网络 (CRN) 中，有效的频谱管理可提供更好的服务质量。频谱是有限的，但频谱的重要性在每一代网络中都在增加。由于无效的频谱分配政策，一些研究表明，大部分许可无线电没有得到有效使用。CRN 是一种智能频谱利用创新，可提供更好的频谱接口。频谱感测以保护对授权用户的干扰的方式检测未使用的频谱。原则上，次用户（SU）接收主用户（PU）信号并将其报告给融合中心进行决策或频谱分配。SU 协同检测 PU 的存在与否。这种类型的频谱感知称为协作频谱感知。然而，这种频谱感知的意义因安全问题而模糊不清。恶意用户可以故意报告有关 PU 存在的误导性信息。因此，在本文中，提出了一种支持向量机学习算法来统计学习恶意用户的行为，并对合法SU和恶意用户进行分类。还集成了粒子群优化算法，以学习最小的可区分恶意用户的能量报告与合法 SU 的偏差。已应用检测概率和检测能量来评估所提出方法的贡献。最后，仿真结果证实，可以从所提出的统计方法中获得更好的频谱管理。