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Identification and Elimination of Abnormal Information in Electromagnetic Spectrum Cognition

  • Haojun Zhao
  • Ruowu Wu
  • Hui Han
  • Xiang Chen
  • Yuyao Li
  • Yun LinEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 279)

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%.

Keywords

Cognitive radio Cooperative spectrum sensing Spectrum sensing data falsification (SSDF) Bayesian learning 

Notes

Acknowledgment

This work is supported by the National Natural Science Foundation of China (61771154), the Fundamental Research Funds for the Central Universities (HEUCFG201830), and the funding of State Key Laboratory of CEMEE (CEMEE2018K0104A).

This paper is also funded by the International Exchange Program of Harbin Engineering University for Innovation-oriented Talents Cultivation.

Meantime, all the authors declare that there is no conflict of interests regarding the publication of this article.

We gratefully thank of very useful discussions of reviewers.

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Haojun Zhao
    • 1
  • Ruowu Wu
    • 2
  • Hui Han
    • 2
  • Xiang Chen
    • 2
  • Yuyao Li
    • 1
  • Yun Lin
    • 1
    Email author
  1. 1.College of Information and Communication EngineeringHarbin Engineering UniversityHarbinChina
  2. 2.State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE)LuoyangChina

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