Convex Optimization
Diagnosing network-wide traffic anomalies
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
A wireless sensor network For structural monitoring
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Mining anomalies using traffic feature distributions
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Sensitivity of PCA for traffic anomaly detection
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Computing Correlation Anomaly Scores Using Stochastic Nearest Neighbors
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
ACM Computing Surveys (CSUR)
Trajectory Outlier Detection: A Partition-and-Detect Framework
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Online Anomaly Prediction for Robust Cluster Systems
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
An accelerated gradient method for trace norm minimization
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Network anomaly detection based on Eigen equation compression
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
ORDEN: outlier region detection and exploration in sensor networks
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Anomaly extraction in backbone networks using association rules
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Accelerated Gradient Method for Multi-task Sparse Learning Problem
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Effective Anomaly Detection in Sensor Networks Data Streams
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Data stream anomaly detection through principal subspace tracking
Proceedings of the 2010 ACM Symposium on Applied Computing
Multi-task feature learning via efficient l2, 1-norm minimization
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Boosting with structure information in the functional space: an application to graph classification
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Distributed detection/localization of change-points in high-dimensional network traffic data
Statistics and Computing
Root cause detection in a service-oriented architecture
Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems
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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.