DocumentCode :
38978
Title :
A Family of Joint Sparse PCA Algorithms for Anomaly Localization in Network Data Streams
Author :
Ruoyi Jiang ; Hongliang Fei ; Jun Huan
Author_Institution :
Adometry Corp., Austin, TX, USA
Volume :
25
Issue :
11
fYear :
2013
fDate :
Nov. 2013
Firstpage :
2421
Lastpage :
2433
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. Furthermore, we extend our joint sparse PCA framework with multidimensional Karhunen Loève Expansion that considers both spatial and temporal domains of data streams to stabilize localization performance. We have performed comprehensive experimental studies of the proposed methods and have compared our methods with the state-of-the-art using three real-world data sets from different application domains. Our experimental studies demonstrate the utility of the proposed methods.
Keywords :
data analysis; network theory (graphs); principal component analysis; PCA-based approaches; anomaly localization; joint sparse PCA algorithms; multidimensional Karhunen Lώve expansion; network data streams; principal component analysis; sparse low-dimensional space; spatial domains; temporal domains; Correlation; Equations; Joints; Principal component analysis; Sparse matrices; Time series analysis; Vectors; Anomaly detection; PCA; anomaly localization; joint sparsity; network data stream; optimization;
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/TKDE.2012.176
Filename :
6295617
Link To Document :
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