Visual analysis of large-scale network anomalies

ISCAS OpenIR

	Visual analysis of large-scale network anomalies
	Liao, Q.; Shi, L.; Wang, C.
	2013
发表期刊	IBM JOURNAL OF RESEARCH AND DEVELOPMENT
ISSN	0018-8646
卷号	57 期号:3-4
摘要	The amount of information flowing across communication networks has rapidly increased. The highly dynamic and complex networks, represented as large graphs, make the analysis of such networks increasingly challenging. In this paper, we provide a brief overview of several useful visualization techniques for the analysis of spatiotemporal anomalies in large-scale networks. We make use of community-based similarity graphs (CSGs), temporal expansion model graphs (TEMGs), correlation graphs (CGs), high-dimension projection graphs (HDPGs), and topology-preserving compressed graphs (TPCGs). CSG is used to detect anomalies based on community membership changes rather than individual nodes and edges and therefore may be more tolerant to the highly dynamic nature of large networks. TEMG transforms network topologies into directed trees so that efficient search is more likely to be performed for anomalous changes in network behavior and routing topology in large dynamic networks. CG and HDPG are used to examine the complex relationship of data dimensions among graph nodes through transformation in a high-dimensional space. TPCG groups nodes with similar neighbor sets into mega-nodes, thus making graph visualization and analysis more scalable to large networks. All the methods target efficient large-graph anomaly visualization from different perspectives and together provide valuable insights.; The amount of information flowing across communication networks has rapidly increased. The highly dynamic and complex networks, represented as large graphs, make the analysis of such networks increasingly challenging. In this paper, we provide a brief overview of several useful visualization techniques for the analysis of spatiotemporal anomalies in large-scale networks. We make use of community-based similarity graphs (CSGs), temporal expansion model graphs (TEMGs), correlation graphs (CGs), high-dimension projection graphs (HDPGs), and topology-preserving compressed graphs (TPCGs). CSG is used to detect anomalies based on community membership changes rather than individual nodes and edges and therefore may be more tolerant to the highly dynamic nature of large networks. TEMG transforms network topologies into directed trees so that efficient search is more likely to be performed for anomalous changes in network behavior and routing topology in large dynamic networks. CG and HDPG are used to examine the complex relationship of data dimensions among graph nodes through transformation in a high-dimensional space. TPCG groups nodes with similar neighbor sets into mega-nodes, thus making graph visualization and analysis more scalable to large networks. All the methods target efficient large-graph anomaly visualization from different perspectives and together provide valuable insights.
收录类别	SCI
部门归属	[Liao, Q.] Cent Michigan Univ, Dept Comp Sci, Mt Pleasant, MI 48859 USA. [Shi, L.] Chinese Acad Sci, State Key Lab Comp Sci, Inst Software, Beijing 100190, Peoples R China. [Wang, C.] IBM Res Div, China Res Lab, Beijing 100193, Peoples R China.
语种	英语
WOS记录号	WOS:000323322800014
引用统计
内容类型	期刊论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16697
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Liao, Q.,Shi, L.,Wang, C.. Visual analysis of large-scale network anomalies[J]. IBM JOURNAL OF RESEARCH AND DEVELOPMENT,2013,57(3-4).
APA	Liao, Q.,Shi, L.,&Wang, C..(2013).Visual analysis of large-scale network anomalies.IBM JOURNAL OF RESEARCH AND DEVELOPMENT,57(3-4).
MLA	Liao, Q.,et al."Visual analysis of large-scale network anomalies".IBM JOURNAL OF RESEARCH AND DEVELOPMENT 57.3-4(2013).