2011 AnomalyLocalizationforNetworkDa

• (Jiang et al., 2011) ⇒ Ruoyi Jiang, Hongliang Fei, and Jun Huan. (2011). “Anomaly Localization for Network Data Streams with Graph Joint Sparse PCA.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020557

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222011%22+Anomaly+Localization+for+Network+Data+Streams+with+Graph+Joint+Sparse+PCA
• http://dl.acm.org/citation.cfm?id=2020408.2020557&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; anomaly localization; data mining; experimentation; network data streams; pca; sparse learning

Abstract

Determining anomalies in data streams that are collected and transformed from various types of networks has recently attracted significant research interest. Principal Component Analysis (PCA) has been extensively applied to detecting anomalies in network data streams. However, none of existing PCA based approaches addresses the problem of identifying the sources that contribute most to the observed anomaly, or anomaly localization. In this paper, we propose novel sparse PCA methods to perform anomaly detection and localization for network data streams. Our key observation is that we can localize anomalies by identifying a sparse low dimensional space that captures the abnormal events in data streams. To better capture the sources of anomalies, we incorporate the structure information of the network stream data in our anomaly localization framework. We have performed comprehensive experimental studies of the proposed methods, and have compared our methods with the state-ofthe-art using three real-world data sets from different application domains. Our experimental studies demonstrate the utility of the proposed methods.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 AnomalyLocalizationforNetworkDa	Hongliang Fei Jun Huan Ruoyi Jiang			Anomaly Localization for Network Data Streams with Graph Joint Sparse PCA				10.1145/2020408.2020557		2011