Traffic Model Analysis for Anomaly Detection

摘要：

Traffic modeling as one of the ways to describe the normal behavior of network traffiC is used to detect anomaly.Due to the self-similar model and multi.fractal modeI are inherently unable to capture the nature of traffiC data in all time scaies.we propose a novel anomaly detection method based on IDC model analysis to describe the characteristic of traffic data more accurately.By studying the influences of anomalous traffic on the estimation of IDC model through wavelet traitsforin modulus maxima.a cumulative deviation is defined to estimate abnormal behavior.The simulation results show that our method is more sensitive to small anomalous traffic than detection methods based on H parameter analysis,and can accurately detect the anomalies Which WOuld not cause the Hurst parameter change evidently.Therefore.it is suite for the early stage detection of anomaly traffic.

关键词： anomaly detection cascade model wavelet transform modulus maxima(WTMM)

作者: Zonglin.Li Guangrnin.Hu Ruqiang.Zhou

会议类型: 国际会议

会议名称: The International Conference Information Computing and Automation(2007国际信息计算与自动化会议)

会议地点: 成都

会议语种:英文

页码: 1434-1437

在线出版日期: 2007-12-19（万方平台首次上网日期，不代表论文的发表时间）