摘要
针对大规模高速网络中传统异常检测算法检测效率、扩充性等不足,提出一种新的异常检测算法,将大规模的高速网络流量汇聚看成信号来处理,通过小波三层聚合算法将其分解成高中低三个频段,再利用小波偏差值算法对影响流量的关键频段进行运算,最终得到可突显流量异常的不同时间窗内偏差值分布.试验分析表明了该算法的有效性和可行性,且检测效率较高,可被用于构建大规模高速网络自动实时在线异常检测系统.
In view of the shortcomings of traditional anomaly detection algorithms in large-scale high-speed network, such as lacks of efficiency and extensibility and so on, a new anomaly detection algorithm was proposed. Large-scale high-speed network traffic was processed as signal, which can be decomposed into high, middle and low frequency bands. Then the key bands affecting network traffic were processed through the wavelet deviation algorithm. Eventually the deviation＇s distribution within different time windows was derived, which can highlight anomalies. The experiments show the effectiveness, feasibility and high detection efficiency of the algorithm, which can be used to build an on-line real-time automatic anomaly detection system in large-scale high-speed network.
出处
《江苏大学学报（自然科学版）》
EI
CAS
北大核心
2008年第1期70-73,共4页
Journal of Jiangsu University：Natural Science Edition
基金
江苏省教育厅高校科学研究基金资助项目(03KJD520073)
关键词
网络
小波偏差值
异常检测
小波多分辨率分析
时间窗
小波三层聚合
network
wavelet deviation
anomaly detection
wavelet multi-resolution analysis
time window
wavelet three-level aggregation
