A Detection Algorithm to Anomaly Network Traffic Based on Spectral Kurtosis Analysis

This paper presents a detection algorithm for anomaly network traffic, which is based on spectral kurtosis analysis. Firstly, we turn network traffic into time-frequency signals at different scales. These time-frequency signals hold the more detailed nature corresponding to different scales. Secondly, the time-frequency signals at different scales are transformed into a series of new time signals by time-frequency analysis theory. These new time signals hold obvious narrowband nature and embody the local properties of network traffic. Thirdly, we calculate the spectral kurtosis values of the new time signals and then perform the feature extractions. As a result, the abnormal network traffic can be correctly identified. Simulation results show that our algorithm is feasible and promising.