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

Furqan H. M., Aygül M. A., Nazzal M., ARSLAN H.

Eurasip Journal on Wireless Communications and Networking, vol.2020, no.1, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 2020 Issue: 1
  • Publication Date: 2020
  • Doi Number: 10.1186/s13638-020-01736-y
  • Journal Name: Eurasip Journal on Wireless Communications and Networking
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, Metadex, zbMATH, Directory of Open Access Journals, Civil Engineering Abstracts
  • Keywords: Cognitive radio, Jamming detection, Machine learning, Physical layer security, Primary user emulation detection, Residual components, Sparse coding, Authentication, Physical layer authentication
  • Istanbul Medipol University Affiliated: Yes


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.