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Recognition and elimination of SSDF attackers in cognitive radio networks

Author

Listed:
  • Fatemeh Zardosht

    (Shiraz University)

  • Mostafa Derakhtian

    (Shiraz University)

  • Ali Jamshidi

    (Shiraz University)

  • Hossein Eshaghi

    (Yazd University)

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.

Suggested Citation

  • Fatemeh Zardosht & Mostafa Derakhtian & Ali Jamshidi & Hossein Eshaghi, 2022. "Recognition and elimination of SSDF attackers in cognitive radio networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 81(1), pages 53-66, September.
    • Handle: RePEc:spr:telsys:v:81:y:2022:i:1:d:10.1007_s11235-022-00935-w
    DOI: 10.1007/s11235-022-00935-w
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    References listed on IDEAS

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    1. Runze Wan & Lixin Ding & Naixue Xiong & Xing Zhou, 2019. "Mitigation strategy against spectrum-sensing data falsification attack in cognitive radio sensor networks," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.
    2. Chu Ji & Qi Zhu, 2021. "Smart contract-based secure cooperative spectrum sensing algorithm," International Journal of Distributed Sensor Networks, , vol. 17(12), pages 15501477211, December.
    3. Jun Wu & Tiecheng Song & Yue Yu & Cong Wang & Jing Hu, 2018. "Sequential cooperative spectrum sensing in the presence of dynamic Byzantine attack for mobile networks," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-32, July.
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