Network Traffic Anomaly Detection Based on Self-Similarity Using HHT and Wavelet Transform

摘要：

Network traffic anomaly detection can be done through the self-similar analysis of network traffic. In this case, the abnormal condition of network can be indicated by investigating if the performance parameters of real time data locate at the acceptable ranges. A common method of estimating self-similar parameter is the Wavelet transform. However, the Wavelet transform fails to exclude the influence of non-stationary signals periodicity and trend term. In view of the fact that Hilbert-Huang Transform (HHT) has unique advantage on non-stationary signal treatment, in this paper, a refined self-similar parameter estimation algorithm is designed through the combination of wavelet analysis and Hilbert-Huang Transform and a set of experiments are run to verify the improvement in the accuracy of parameter estimation and network traffic anomaly detection.

关键词： anomaly detection HHT EMD wavelet transform self-Similar

作者: Xiaorong Cheng Kun Xie Dong Wang

作者单位: School of Computer Science and Technology North China Electric Power University Baoding,China

会议类型: 国际会议

会议名称: The Fifth International Conference on Information Assurance and Security(第五届信息保障与安全国际会议)

会议地点: 西安

会议语种:英文

页码: 710-713

在线出版日期: 2009-08-18（万方平台首次上网日期，不代表论文的发表时间）