Network Traffic Anomaly Detection Based on Self-similarity Using FRFT

被引：0

作者：

Ye, Xiaolong ^{[1
]}

Lan, Julong ^{[1
]}

Huang, Wanwei ^{[1
]}

机构：

[1] Technol R&D Ctr, Dept Natl Digital Switching Syst Engn, Zhengzhou, Henan Province, Peoples R China

来源：

PROCEEDINGS OF 2013 IEEE 4TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS) | 2012年

关键词：

component; self-similarity; anomaly detection; wavelet; FRFT; Hurst parameter;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Since traditional abnormal detection methods have poor performance, and Hurst parameter estimation was affected by non-stationary traffic. An abnormal detection method based on Hurst parameter estimation using Fractional Fourier Transform (FRFT) was implemented. The experimental results show that FRFT estimation method was not affected by non-stationary time series and has better performance on Hurst estimation. We also verify the improvement in the network traffic anomaly detection.

引用

页码：837 / 840

页数：4

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