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Research on spectrum sensing data falsification attack detection algorithm in cognitive Internet of Things

Author

Listed:
  • Liu Miao

    (Northeast Petroleum University-Qinhuangdao)

  • Xu Di

    (Northeast Petroleum University)

  • Zhuo-Miao Huo

    (Northeast Petroleum University)

  • Zhen-Xing Sun

    (Northeast Petroleum University-Qinhuangdao
    Northeastern University)

Abstract

The Internet of Things (IoT) is a new paradigm for connecting various heterogeneous networks. Cognitive radio (CR) adopts cooperative spectrum sensing (CSS) to realize the secondary utilization of idle spectrum by unauthorized IoT devices, allowing IoT objects can effectively use spectrum resources. However, the abnormal IoT devices in the cognitive Internet of Things will disrupt the CSS process. For this attack, we propose a spectrum sensing strategy based on weighted combining of the hidden Markov model. The method uses the hidden Markov model to detect the probability of malicious attacks at each node and reports to the Fusion Center (FC), which evaluates the submitted observations and assigns reasonable weight to improve the accuracy of the sensing results. Simulation results show that the algorithm proposed has a higher detection probability and a lower false alarm probability than other algorithms, which can effectively resist spectrum sensing data falsification (SSDF) attacks in cognitive Internet of Things and improve the performance of IoT devices.

Suggested Citation

  • Liu Miao & Xu Di & Zhuo-Miao Huo & Zhen-Xing Sun, 2022. "Research on spectrum sensing data falsification attack detection algorithm in cognitive Internet of Things," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 80(2), pages 227-238, June.
    • Handle: RePEc:spr:telsys:v:80:y:2022:i:2:d:10.1007_s11235-022-00896-0
    DOI: 10.1007/s11235-022-00896-0
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