A Reliable Spectrum Sensing Method Based on Deep Learning for Primary User Emulation Attack Detection in Cognitive Radio Network
Mingdong Xu, Yanlong Zhao, Rui Zhang, Zhendong Yin, Zhilu Wu
Abstract
Cognitive radio network (CRN) is highly vulnerable to malicious attacks, which can significantly impact the effectiveness and reliability of spectrum sensing. Existing deep learning-based works have proven ineffective in accurately recognizing an unknown attacker due to the prior information about the attacker for training purposes is not easily available in advance. To address the challenge of reliably identifying the known primary user (PU) and the unknown attacker, we combine deep learning with extreme value theory (EVT) to propose a reliable spectrum sensing method for primary user emulation attack (PUEA) detection. Importantly, our method does not rely on acquiring the prior information about the attacker during the training phase. The proposed deep learning-based method not only enables the secondary user (SU) to accurately confirm the presence of PU but also effectively identifies unknown attackers. In addition, the network structure of proposed method is redesigned to extract diverse multi-domain features. The simulation results indicate that the proposed method exhibits remarkable performance under the presence of unknown attacker.