Deep Learning-Assisted Detection of PUE and Jamming Attacks in Cognitive Radio Systems


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

92nd IEEE Vehicular Technology Conference, VTC 2020-Fall, Virtual, Victoria, Canada, 18 - 16 November 2020, vol.2020-November identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 2020-November
  • Doi Number: 10.1109/vtc2020-fall49728.2020.9348579
  • City: Virtual, Victoria
  • Country: Canada
  • Keywords: Cognitive radio, deep learning, emulation detection, jamming detection, physical layer security, primary user
  • Istanbul Medipol University Affiliated: Yes

Abstract

Cognitive radio (CR)-based internet of things systems can be considered as an efficient solution for futuristic smart technologies. However, CRs are naturally vulnerable to two major security threats; primary user emulation (PUE) and jamming attacks. Machine learning has been recently applied to the detection of these attacks. Still, the need for feature extraction required by machine learning techniques restrains the full exploitation of raw data. To alleviate this need, this paper proposes one-dimensional deep learning as a framework for identifying such attacks. Simulations show the ability of the proposed algorithm to detect these attacks with high performance.