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| Scalable network traffic visualization using compressed graphs | |
| Shi, Lei (1); Liac, Qi (2); Sun, Xiaohua (3); Chen, Yarui (4); Lin, Chuang (4) | |
| 2013 | |
| Conference Name | 2013 IEEE International Conference on Big Data, Big Data 2013 |
| Pages | 606-612 |
| Conference Date | October 6, 2013 - October 9, 2013 |
| Conference Place | Santa Clara, CA, United states |
| Indexed Type | CPCI ; EI |
| Publish Place | IEEE Computer Society, 2001 L Street N.W., Suite 700, Washington, DC 20036-4928, United States |
| ISBN | 9781479912926 |
| Department | (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 |
| English Abstract | 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. |
| Keyword | Security Visualization Graph Compression |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16543 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation 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. |
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