2010 MetricForensicsaMultiLevelAppro

• (Henderson et al., 2010) ⇒ Keith Henderson, Tina Eliassi-Rad, Christos Faloutsos, Leman Akoglu, Lei Li, Koji Maruhashi, B. Aditya Prakash, and Hanghang Tong. (2010). “Metric Forensics: A Multi-level Approach for Mining Volatile Graphs.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835828

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• http://scholar.google.com/scholar?q=%22Metric+forensics%3A+a+multi-level+approach+for+mining+volatile+graphs%22+2010
• http://portal.acm.org/citation.cfm?id=1835828&preflayout=flat#citedby

Quotes

Abstract

Advances in data collection and storage capacity have made it increasingly possible to collect highly volatile graph data for analysis. Existing graph analysis techniques are not appropriate for such data, especially in cases where streaming or near-near-real-time results are required. An example that has drawn significant research interest is the cyber-security domain, where internet communication traces are collected and real-time discovery of events, behaviors, patterns, and anomalies is desired. We propose MetricForensics, a scalable framework for analysis of volatile graphs. MetricForensics combines a multi-level "drill down" approach, a collection of user-selected graph metrics, and a collection of analysis techniques. At each successive level, more sophisticated metrics are computed and the graph is viewed at finer temporal resolutions. In this way, MetricForensics scales to highly volatile graphs by only allocating resources for computationally expensive analysis when an interesting event is discovered at a coarser resolution first. We test MetricForensics on three real-world graphs: an enterprise IP trace, a trace of legitimate and malicious network traffic from a research institution, and the MIT Reality Mining proximity sensor data. Our largest graph has ~3M vertices and ~32M edges, spanning 4.5 days. The results demonstrate the scalability and capability of MetricForensics in analyzing volatile graphs; and highlight four novel phenomena in such graphs: elbows, broken correlations, prolonged spikes, and lightweight stars.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 MetricForensicsaMultiLevelAppro	B. Aditya Prakash Christos Faloutsos Lei Li Leman Akoglu Hanghang Tong Keith Henderson Tina Eliassi-Rad Koji Maruhashi			Metric Forensics: A Multi-level Approach for Mining Volatile Graphs		KDD-2010 Proceedings	http://eliassi.org/papers/henderson-kdd10.pdf	10.1145/1835804.1835828		2010