Recognition and elimination of SSDF attackers in cognitive radio networks

Fatemeh Zardosht (), Mostafa Derakhtian (), Ali Jamshidi () and Hossein Eshaghi ()
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Fatemeh Zardosht: Shiraz University
Mostafa Derakhtian: Shiraz University
Ali Jamshidi: Shiraz University
Hossein Eshaghi: Yazd University

Telecommunication Systems: Modelling, Analysis, Design and Management, 2022, vol. 81, issue 1, No 5, 53-66

Abstract: Abstract The nature of cognitive radio (CR) technology creates a lot of opportunities for attackers. When an attack occurs, the function of the primary network is affected and thus the overall system performance will be reduced. In the present paper, we introduce and simulate a novel method for identifying spectral sensing data falsification (SSDF) attack and recognizing the malicious users (MU), which we refer to as “Recognition and Elimination of SSDF Attackers”. Our proposed scheme uses the generalized likelihood ratio test (GLRT) approach for solving the MUs detection problem. In this method, we do not need previous information about the network and number of the MUs and secondary users (SUs). In addition to detecting the occurrence of an attack, our method can recognize attackers. By recognizing the MUs, their negative effect will be eliminated and the cognitive radio network (CRN) performance will return to normal condition. Consequently, our scheme can save resources by identifying the strategy of the known attackers. Simulation results reveal that our detection and recognition scheme is better than some of methods available.

Keywords: Cognitive radio network; Malicious user primary user; Secondary user; Spectral sensing data falsification (SSDF) attacks (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11235-022-00935-w

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