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Physical Layer Security in Cognitive Radio Networks: Detection and Mitigation of Primary User Emulation Attacks

Ammar, Mahmod; Riley, Nick; Mehdawi, Meftah; Fanan, Anwar; Zolfaghari, Mahsa

Authors

Mahmod Ammar

Nick Riley

Meftah Mehdawi

Anwar Fanan

Mahsa Zolfaghari



Abstract

Cognitive Radio Networks (CR) is an advanced growing technique and a promising technology for the upcoming generation of the wireless networks in order to efficiently utilize the limited spectrum resources and satisfy the rapidly increasing demand for wireless applications and services. Deployment of such networks is hindered by the vulnerabilities that these networks are exposed to, in this paper we focus on security problems arising from Primary User Emulation Attacks (PUEA) in CR networks. We present a comprehensive introduction to primary user emulation attacks, from the attacking rationale and its impact on CR networks, to detection and defense approaches. We have setup the system model using Matlab software, we have used the Neyman-Pearson composite hypothesis test NPCHT to obtain the hypothesis test and detect the PUEA. In order to secure CR networks against PUE attacks, we considered the power received at the secondary receiver. Simulation results proved that using the NPCHT it is possible to keep the probability of success of PUEA low depends on the threshold value. The number of malicious users in the system can significantly increase the probability of false alarm in the network, Also it shows that there is a range of network radii in which PUEA are most successful.

Start Date Jan 7, 2015
Publication Date Jan 7, 2015
Pages 86-91
Series Number http://dx.doi.org/10.15242/IIE.E0115036
Book Title International Conference on Artificial Intelligence, Energy and Manufacturing Engineering (ICAEME'2015)
Chapter Number 1
ISBN 9789384422059
APA6 Citation Ammar, M., Riley, N., Mehdawi, M., Fanan, A., & Zolfaghari, M. (2015). Physical Layer Security in Cognitive Radio Networks: Detection and Mitigation of Primary User Emulation Attacks. In International Conference on Artificial Intelligence, Energy and Manufacturing Engineering (ICAEME'2015), 86-91. doi:10.15242/IIE.E0115036
DOI https://doi.org/10.15242/IIE.E0115036
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