2018 Vol.1 Oct. No.5

• Title: An Improved Fractional Fourier Transform Network Information Anomaly Behavior Feature Detection Algorithm
• Name: Theodore Ulysses
• Company: Dhurakij Pundit University
• Abstract:

  This paper studies the feature extraction and detection of abnormal information in network information. As the network attack becomes more and more concealed, the abnormal behavior of network information, as a form of data signal sequence, exists in the network information space, and has strong interference. It is difficult to detect by traditional algorithms. Based on fractional Fourier transform, an improved algorithm for detecting abnormal behavior of networks under low SNR is proposed. Using the fourth-order cumulant slice to analyze the energy aggregation and noise suppression of the FM signal, in the post-processing of fractional Fourier transform detection, the fourth-order cumulant post-processing operator is introduced, and the fourth-order cumulant slice operator is used to analyze the network anomaly. The information feature signal performs post-energy aggregation on the fractional Fourier domain, increasing the signal accumulation and effectively suppressing background interference. Simulation experiments show that the improved algorithm has strong noise interference suppression performance, can maximize the characteristics of abnormal behavior in cyberspace, and the detection efficiency is improved significantly. The research results can be effectively applied to the field of network information confrontation and security.

• Keyword: Fractional Fourier transform, Network; Feature; Detection;
• DOI: 10.12250/jpciams2018050118
• Citation form: Theodore Ulysses.An Improved Fractional Fourier Transform Network Information Anomaly Behavior Feature Detection Algorithm[J]. Computer Informatization and Mechanical System, 2018, vol. 1, pp. 7-12.

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