Advanced Hybrid Information Processing. Second EAI International Conference, ADHIP 2018, Yiyang, China, October 5-6, 2018, Proceedings

Research Article

Identification and Elimination of Abnormal Information in Electromagnetic Spectrum Cognition

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  • @INPROCEEDINGS{10.1007/978-3-030-19086-6_9,
        author={Haojun Zhao and Ruowu Wu and Hui Han and Xiang Chen and Yuyao Li and Yun Lin},
        title={Identification and Elimination of Abnormal Information in Electromagnetic Spectrum Cognition},
        proceedings={Advanced Hybrid Information Processing. Second EAI International Conference, ADHIP 2018, Yiyang, China, October 5-6, 2018, Proceedings},
        proceedings_a={ADHIP},
        year={2019},
        month={5},
        keywords={Cognitive radio Cooperative spectrum sensing Spectrum sensing data falsification (SSDF) Bayesian learning},
        doi={10.1007/978-3-030-19086-6_9}
    }
    
  • Haojun Zhao
    Ruowu Wu
    Hui Han
    Xiang Chen
    Yuyao Li
    Yun Lin
    Year: 2019
    Identification and Elimination of Abnormal Information in Electromagnetic Spectrum Cognition
    ADHIP
    Springer
    DOI: 10.1007/978-3-030-19086-6_9
Haojun Zhao1, Ruowu Wu2, Hui Han2, Xiang Chen2, Yuyao Li1, Yun Lin1,*
  • 1: Harbin Engineering University
  • 2: State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE)
*Contact email: linyun_phd@hrbeu.edu.cn

Abstract

The electromagnetic spectrum is an important national strategic resource. Spectrum sensing data falsification (SSDF) is an attack method that destroys cognitive networks and makes them ineffective. Malicious users capture sensory nodes and tamper with data through cyber attacks, and make the cognitive network biased or even completely reversed. In order to eliminate the negative impact caused by abnormal information in spectrum sensing and ensure the desired effect, this thesis starts with the improvement of the performance of cooperative spectrum sensing, and constructs a robust sensing user evaluation reference system. At the same time, considering the dynamic changes of user attributes, the sensory data is identified online. Finally, the attacker identification and elimination algorithm is improved based on the proposed reference system. In addition, this paper verifies the identification performance of the proposed reference system through simulation. The simulation results show that the proposed reference system still maintain a good defense effect even if the proportion of malicious users in the reference is greater than 50%.