Cognitive radio as a key technology is taken into consideration widely to cope with the shortage of spectrum in wireless networks. One of the major challenges to realization of CR networks is security. The most important of these threats is primary user emulation attack, thus malicious user attempts to send a signal same as primary user's signal to deceive secondary users and prevent them from sending signals in the spectrum holes. Meanwhile, causing traffic in CR network, malicious user obtains a frequency band to send their information. In this thesis, a method to identify primary user emulation attack is proposed. According to this method, primary users and malicious users are distinguished by clustering. In this method, the number of active users is recognized in the CR network by clustering. Indeed, by using Dirichlet process mixture model classification based on the Bayesian Nonparametric method, primary users are clustered. In addition, to achieve higher convergence rate, Chinese restaurant process method to initialize and non-uniform sampling is applied to select clusters parameter.
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