Primary user emulation and jamming attack detection in cognitive radio via sparse coding

作     者：Furqan, Haji M. Aygul, Mehmet A. Nazzal, Mahmoud Arslan, Huseyin 

作者机构：Istanbul Medipol Univ Sch Engn & Nat Sci TR-34810 Istanbul Turkey Univ S Florida Dept Elect Engn Tampa FL 33620 USA 

出 版 物：《EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING》 (EURASIP无线通信与网络杂志)

年 卷 期：2020年第2020卷第1期

页      面：1-19页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：TUBITAK, (119E433) Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK 

主　　题：Cognitive radio Jamming detection Machine learning Physical layer security Primary user emulation detection Residual components Sparse coding Authentication Physical layer authentication 

摘      要：Cognitive radio is an intelligent and adaptive radio that improves the utilization of the spectrum by its opportunistic sharing. However, it is inherently vulnerable to primary user emulation and jamming attacks that degrade the spectrum utilization. In this paper, an algorithm for the detection of primary user emulation and jamming attacks in cognitive radio is proposed. The proposed algorithm is based on the sparse coding of the compressed received signal over a channel-dependent dictionary. More specifically, the convergence patterns in sparse coding according to such a dictionary are used to distinguish between a spectrum hole, a legitimate primary user, and an emulator or a jammer. The process of decision-making is carried out as a machine learning-based classification operation. Extensive numerical experiments show the effectiveness of the proposed algorithm in detecting the aforementioned attacks with high success rates. This is validated in terms of the confusion matrix quality metric. Besides, the proposed algorithm is shown to be superior to energy detection-based machine learning techniques in terms of receiver operating characteristics curves and the areas under these curves.