Identification and Elimination of Abnormal Information in Electromagnetic Spectrum Cognition
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%.
KeywordsCognitive radio Cooperative spectrum sensing Spectrum sensing data falsification (SSDF) Bayesian learning
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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