An Improved Abnormal Behavior Feature Detection Algorithm of Network Information Based on Fractional Fourier Transform

Abstract:

The feature extraction and detection problem of network information abnormal behavior was researched in this paper. The network attack tended to ambiguity in hidden, and the abnormal behavior of network information is referred as a data signal series, and it was existed in the network information space with strong interference. Traditional detection method was hard to detect the abnormal signal. On the basis of fractional Fourier transform (FRFT), an improved abnormal behavior feature detection algorithm of network information was proposed. The properties such as energy gathering and noise suppression of 4-order cumulant slice were taken in advantage. In the post processing of fractional Fourier transform detection, the post processing operator of 4 order cumulant was introduced in the detection algorithm, the post energy was gathered in the fractional Fourier domain, the signal accumulation was likely to be increased, and the interference noise could be restrained effectively. Simulation results show that the improved algorithm has perfect noise suppression performance, and it can detect and extract the abnormal behavior feature in the network space to maximum. The detection performance is improved greatly, and the research result can be applied in the network information warfare and network security areas.

• Periodical: Applied Mechanics and Materials (Volumes 513-517)
• Pages: 2408-2411
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.513-517.2408
• Online since: February 2014
• Authors: Qi Hua Peng*
• Keywords: Feature Detection, Fractional Fourier Transform (FrFT), Network Information

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