Abnormal Information Identification and Elimination in Cognitive Networks

doi:10.23940/ijpe.18.10.p3.22712279

Abstract

Abstract:

The electromagnetic spectrum is an important national strategic resource, and spectrum sensing data falsification (SSDF) is an attack method that destroys the cognitive network and makes it impossible to be used effectively. Malicious users capture the sensory nodes through cyber attacks, virus intrusions, etc., tampering with the perceived data and making the cognitive network biased or even completely reversed. In order to eliminate the negative effects caused by the identification and elimination of abnormal information in the electromagnetic spectrum in multi-user collaboration and to ensure the desired effect, this paper studies and constructs a robust cognitive user evaluation reference system based on improving the performance of cooperative spectrum sensing. The impact of attack behavior on the reference frame is greatly reduced. At the same time, the attacker’s identification and elimination algorithm are improved, and the influence of abnormal data on the perceived performance under the combined effect is eliminated.

Submitted on May 17, 2018; Revised on July 8, 2018; Accepted on August 17, 2018
References: 20

Ruowu Wu, Xiang Chen, Hui Han, Haojun Zhao, and Yun Lin. Abnormal Information Identification and Elimination in Cognitive Networks [J]. Int J Performability Eng, 2018, 14(10): 2271-2279.

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