# Multiple PUE Attack Detection in Cooperative Mobile Cognitive Radio Networks
## 1 Introduction  
Emerging telecommunication technologies have become a crucial part of our daily lives, whether for work, study, or entertainment. Nevertheless, there is an increasing need for more bandwidth, especially in wireless networks. Cognitive radio networks (CRNs) are an alternative to increase performance and use the frequency spectrum, whereas the primary user (PU) does not use the spectrum at specific frequencies and times. A secondary user (SU) uses the resources while there is no PU in the CRN, increasing the spectrum usage and helping with the spectrum scarcity problem. By enabling dynamic spectrum access, cognitive radios significantly enhance spectrum utilization and improve the performance of wireless networks [1]. A CRN operates on the principle of spectrum sensing, where cognitive devices continuously monitor the spectrum to detect unused frequencies, and there is a vulnerability that attackers can use because decisions are based on that sensing. The CRN is vulnerable to some attacks, mainly because there is an SU in the system, and they can be malicious users. The CRN is also susceptible to traditional attacks. Nevertheless, new attacks can affect functions like spectrum sensing, mobility, management, or sharing, the main aspects of the CRN, causing network malfunctions, affecting PU and SUs, and decreasing spectrum availability. The malicious users obtain access to a frequency and can use it to communicate or influence the network [2]. One attack affecting the CRN is the Primary User Emulation attack. An adversary intentionally mimics the signal of a PU, misleading SUs into believing that a licensed user isoccupying the frequency band. This forces the SU to release the channel, even when it is not an authorized user. The PUE can lead to inefficient spectrum utilization, as secondary users are unnecessarily displaced from frequencies that could otherwise be used. The disruption caused by PUE results in degraded quality of service (QoS) and connectivity issues for PU [3]. Multiple SUs sense the PU spectrum in a cooperative spectrum-sensing (CSS) network to counter PUE attacks. This increases the sensing capabilities and coverage and can work with a centralized or decentralized architecture. In the first one, the local sensing data or PU presence decisions are sent to a fusion center (FC), which makes a global decision of PUE presence based on the data. It is more effective for the CSS than a single SU for sensing, and it has been used against selfish PUE attacks, but the malicious user can attack with different strategies. The first method used to analyze the users was to measure the energy. Nevertheless, with time, attackers learn to change power parameters, making it difficult to recognize an attack with an energy detector [4]. Research into counteracting PUE attacks includes strategies like advanced spectrum-sensing algorithms in a single or cooperative environment, authentication protocols, and intrusion detection systems. These countermeasures aim to enhance the reliability of spectrum sensing, ensure the integrity of primary user identification, and minimize the impact of malicious interference. Evaluating the effectiveness of these strategies in different network environments and attack scenarios is a critical aspect of advancing the security of cognitive radio networks. Various PUEA prevention strategies are suggested for a PU working as a TV transmitter, mobile FM microphones, and methods like Fenton Approximation, Fingerprint Verification, or applying artificial intelligence (AI) techniques like ANN [5]. Although some techniques to detect PUE have been implemented, obtaining different detection performances, the literature does not include papers that work with multiple PUE attacks in a single environment and which are implemented on radio devices. This paper proposes an AI technique for detecting multiple PUE attacks in a cooperative CSS MCRN with SDR devices such as a PU, SU, and MU.