# Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System
## 1 Introduction  
Spectrum sensing is a key link of cognitive radio and the perceived performance will directly affect the performance of the whole cognitive radio system [1]. At present, the commonly-used spectrum sensing technologies can be divided into three categories: single-user spectrum sensing, distributed collaborative spectrum sensing, and centralized collaborative spectrum sensing. Single-user spectrum sensing technology can only rely on its own perception results, and thus the problems of hidden terminals exist [2]. Distributed collaborative spectrum sensing technology can rely on the perception results of neighboring cognitive users to achieve an increased sensing accuracy, but the method will greatly increase the complexity of the terminal design [3]. The centralized collaborative spectrum sensing technology can greatly improve the perceived precision because it can obtain the perceived result of all the cognitive users in the data fusion center. Furthermore, the demands of the sensing terminal design are not high, [3] and therefore centralized collaborative spectrum sensing technology has drawn more extensive attention. In the centralized collaborative spectrum sensing system, the multi-user perceptual architecture of spectrum resources can greatly improve the sensing accuracy of the data fusion center. However, malicious users will seriously affect the reliability of the system, so the real-time monitoring of the reliability of multi-users is a key problem that the centralized collaborative spectrum sensing system needs to solve. The malicious cognitive users can usually be divided into two categories according to the probability of the malicious cognitive user sending a wrong perceived result. The first category malicious users (I-MUs) refers to those which are caused by equipment failure, causing them to send a wrongly perceived result at a large probability. The second category of malicious users (II-MUs) refers to the cognitive users, who want to deliberately interfere with the normal operations of the system. In order to enhance their own concealment, II-MUs usually send tampered perceived results with a smaller probability. This behavior is called Spectrum Sensing Data False (SSDF) attacks [4]. In order to interfere with the main user or exclusive bands, these cognitive users tamper with their own perceived results, report tampered spectrum sensing information to the data fusion center, disturb the centralized cooperative spectrum sensing algorithm to determine the true spectral state and reduce the perceived performance. The traditional fusion algorithm can identify the first category of malicious cognitive users (I-MU) with high attack probability, but can hardly identify the second category of malicious cognitive users (II-MU). In order to improve the reliability of the centralized collaborative spectrum sensing system, it is necessary to solve the SSDF problem. A study [5] proposed a \"stripping onion\" approach to combat SSDF attacks, in which the central node of the cognitive radio network compares the suspicious degree of the cognitive user with the detection threshold to identify the malicious cognitive user and rejects the perceived result of the malicious cognitive user from the decision. The algorithm needs prior master knowledge of the channel usage rules, or the ability to detect malicious cognitive users will be severely reduced. However, prior knowledge of channel usage is not readily available in reality. Reference [6] proposed a weighted order probability ratio test method based on prestige in order to detect malicious cognitive users and achieve reliable spectrum sensing in the joint spectrum sensing process. However, the algorithm needs to know the prior probabilities of each cognitive user's perceived results, resulting in the computational complexity and communication overhead being too large. Reference [7] proposed a confidence-based colony multi-ant random walk (CARW) algorithm to identify the malicious cognitive users.