Scalable network traffic visualization using compressed graphs

ISCAS OpenIR

	Scalable network traffic visualization using compressed graphs
	Shi, Lei (1); Liac, Qi (2); Sun, Xiaohua (3); Chen, Yarui (4); Lin, Chuang (4)
	2013
会议名称	2013 IEEE International Conference on Big Data, Big Data 2013
页码	606-612
会议日期	October 6, 2013 - October 9, 2013
会议地点	Santa Clara, CA, United states
收录类别	CPCI ; EI
出版地	IEEE Computer Society, 2001 L Street N.W., Suite 700, Washington, DC 20036-4928, United States
ISBN	9781479912926
部门归属	(1) State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, China; (2) Department of Computer Science, Central Michigan University, United States; (3) College of Design and Innovation, Tongji University, China; (4) Department of Computer Science and Technology, Tsinghua University, China
摘要	The visualization of complex network traffic involving a large number of communication devices is a common yet challenging task. Traditional layout methods create the network graph with overwhelming visual clutter, which hinders the network understanding and traffic analysis tasks. The existing graph simplification algorithms (e.g. community-based clustering) can effectively reduce the visual complexity, but lead to less meaningful traffic representations. In this paper, we introduce a new method to the traffic monitoring and anomaly analysis of large networks, namely Structural Equivalence Grouping (SEG). Based on the intrinsic nature of the computer network traffic, SEG condenses the graph by more than 20 times while preserving the critical connectivity information. Computationally, SEG has a linear time complexity and supports undirected, directed and weighted traffic graphs up to a million nodes. We have built a Network Security and Anomaly Visualization (NSAV) tool based on SEG and conducted case studies in several real-world scenarios to show the effectiveness of our technique. © 2013 IEEE.; The visualization of complex network traffic involving a large number of communication devices is a common yet challenging task. Traditional layout methods create the network graph with overwhelming visual clutter, which hinders the network understanding and traffic analysis tasks. The existing graph simplification algorithms (e.g. community-based clustering) can effectively reduce the visual complexity, but lead to less meaningful traffic representations. In this paper, we introduce a new method to the traffic monitoring and anomaly analysis of large networks, namely Structural Equivalence Grouping (SEG). Based on the intrinsic nature of the computer network traffic, SEG condenses the graph by more than 20 times while preserving the critical connectivity information. Computationally, SEG has a linear time complexity and supports undirected, directed and weighted traffic graphs up to a million nodes. We have built a Network Security and Anomaly Visualization (NSAV) tool based on SEG and conducted case studies in several real-world scenarios to show the effectiveness of our technique. © 2013 IEEE.
关键词	Security Visualization Graph Compression
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16543
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Shi, Lei ,Liac, Qi ,Sun, Xiaohua ,et al. Scalable network traffic visualization using compressed graphs[C]. IEEE Computer Society, 2001 L Street N.W., Suite 700, Washington, DC 20036-4928, United States,2013:606-612.