The nature of cognitive radio (CR) technology creates a lot of opportunities for attackers. When an attack occurs, the function of the primary network is affected and thus the overall system performance will be reduced. In the present paper, we introduce and simulate a novel method for identifying spectral sensing data falsification (SSDF) attack and recognizing the malicious users (MU), which we refer to as “Recognition and Elimination of SSDF Attackers”. Our proposed scheme uses the generalized likelihood ratio test (GLRT) approach for solving the MUs detection problem. In this method, we do not need previous information about the network and number of the MUs and secondary users (SUs). In addition to detecting the occurrence of an attack, our method can recognize attackers. By recognizing the MUs, their negative effect will be eliminated and the cognitive radio network (CRN) performance will return to normal condition. Consequently, our scheme can save resources by identifying the strategy of the known attackers. Simulation results reveal that our detection and recognition scheme is better than some of methods available.

认知无线电 (CR) 技术的本质为攻击者创造了很多机会。当攻击发生时，会影响主网络的功能，从而降低整体系统性能。在本文中，我们介绍并模拟了一种识别光谱传感数据伪造（SSDF）攻击和识别恶意用户（MU）的新方法，我们称之为“识别和消除SSDF攻击者”。我们提出的方案使用广义似然比检验 (GLRT) 方法来解决 MU 检测问题。在这种方法中，我们不需要关于网络和 MU 和次要用户 (SU) 数量的先前信息。除了检测攻击的发生之外，我们的方法还可以识别攻击者。通过识别 MU，它们的负面影响将被消除，认知无线电网络 (CRN) 性能将恢复正常。因此，我们的方案可以通过识别已知攻击者的策略来节省资源。仿真结果表明，我们的检测和识别方案优于一些可用的方法。