Detecting malicious clients in ISP networks using HTTP connectivity graph and flow information

Lei Liu, Sabyasachi Saha, R. Torres, Jianpeng Xu, P. Tan, A. Nucci, M. Mellia
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引用次数: 24

Abstract

This paper considers an approach to identify previously undetected malicious clients in Internet Service Provider (ISP) networks by combining flow classification with a graph-based score propagation method. Our approach represents all HTTP communications between clients and servers as a weighted, near-bipartite graph, where the nodes correspond to the IP addresses of clients and servers while the links are their interconnections, weighted according to the output of a flow-based classifier. We employ a two-phase alternating score propagation algorithm on the graph to identify suspicious clients in a monitored network. Using a symmetrized weighted adjacency matrix as its input, we show that our score propagation algorithm is less vulnerable towards inflating the malicious scores of popular Web servers with high in-degrees compared to the normalization used in PageRank, a widely used graph-based method. Experimental results on a 4-hour network trace collected by a large Internet service provider showed that incorporating flow information into score propagation significantly improves the precision of the algorithm.
利用HTTP连接图和流量信息检测ISP网络中的恶意客户端
本文研究了一种将流量分类与基于图的分数传播方法相结合的方法来识别互联网服务提供商(ISP)网络中以前未检测到的恶意客户端。我们的方法将客户端和服务器之间的所有HTTP通信表示为加权的近二部图,其中节点对应于客户端和服务器的IP地址,而链接是它们的互连,根据基于流的分类器的输出进行加权。我们在图上采用两阶段交替得分传播算法来识别监控网络中的可疑客户端。使用对称加权邻接矩阵作为输入,我们表明,与PageRank(一种广泛使用的基于图的方法)中使用的规范化方法相比,我们的分数传播算法不太容易受到高in度流行Web服务器的恶意分数膨胀的影响。在某大型互联网服务提供商采集的4小时网络轨迹上的实验结果表明,将流量信息加入到分数传播中可以显著提高算法的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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