摘要
根据网络流量在大时间尺度上的自相似性,以及在小时间尺度上异常流量、Lipschitz正则性与小波变换模极大值三者之间的关系,提出基于小波分析的网络流量异常检测方法.设计了采用该方法检测网络流量异常的模型,解决了方法实现过程中小波选择、模极大值曲线衰减判断、Hurst指数与Lipschitz指数求解等一些关键问题.实验表明,提出的方法能够较好的发现网络流量异常事件并定位异常发生时刻.
According to the self-similarity of network traffic in large scale, and the relationship among network traffic anomaly in small scale, Lipschitz regularity and wavelet transform modulus maxima, one anomaly detection based on wavelet analysis was proposed. Key issues of the choice of wavelet , judgment of the decay of modulus maxima curve, calculation of Hurst and Lipschitz were resolved, and software model for the method was designed. The experimental results show that the proposed method can find out network vattic anomaly in time, and locate the traffic anomaly time well.
出处
《小型微型计算机系统》
CSCD
北大核心
2010年第1期55-61,共7页
Journal of Chinese Computer Systems
基金
国家"八六三"高技术研究发展计划项目(2007AA10Z309)资助
