基于流量信息结构的异常检测

由于人们对网络流量规律的认识还不够深入,大型高速网络流量的异常检测仍然是目前测量领域研究的一个难点问题.通过对网络流量结构和流量信息结构的研究发现,在一定范围内,正常网络流量的IP、端口等具有重尾分布和自相似特性等较为稳定的流量结构,这种结构对应的信息熵值较为稳定.异常流量和抽样流量的信息熵值以正常流量信息熵值为中心波动,构成以IP、端口和活跃IP数量为维度的空间信息结构.据此对流量进行建模,提出了基于流量信息结构的支持向量机(support vector machine,简称SVM)的二值分类算法,其核心是将流量异常检测转化为基于SVM的分类决策问题.实验结果表明,该算法具有很高的检测效率,还初步验证了该算法的抽样检测能力.因此,将该算法应用到大型高速骨干网络具有实际意义.

Anomaly Detection Based on Traffic Information Structure

Due to the fact that the nature of network traffic is not fully and understood, large-scale, high-speed network traffic anomaly detection in an idea is a difficult problem to solve. According to the analysis of the network traffic structure and traffic information structure, it is found that in a certain range, the IP address and port distributions exhibit heavy tail and self-similar characteristics. The normal network traffic has a relatively stable structure. This structure corresponds to a more stable value of information entropy. Abnormal traffic and sample traffic of information entropy fluctuates by using the normal traffic as the center, and forms the structure of spatial information of IP, port, and IP number of active dimensions. Based on this discovery, the paper proposes a novel traffic classification algorithm, based on support vector machine (SVM) method, that transforms the traffic anomaly detection issue to a SVM-based classification decision issue. The experimental results not only evaluate its accuracy and efficiency, but also show its ability to detect on sampled traffic, which is very important for the traffic data reduction and efficient anomaly detection of high speed networks.