Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Matrix computations (3rd ed.)
Methods for modifying matrix factorizations.
Methods for modifying matrix factorizations.
Diagnosing network-wide traffic anomalies
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Mining anomalies using traffic feature distributions
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Estimation of High-Density Regions Using One-Class Neighbor Machines
IEEE Transactions on Pattern Analysis and Machine Intelligence
InteMon: continuous mining of sensor data in large-scale self-infrastructures
ACM SIGOPS Operating Systems Review
Data streams: algorithms and applications
Foundations and Trends® in Theoretical Computer Science
Data Streams: Models and Algorithms (Advances in Database Systems)
Data Streams: Models and Algorithms (Advances in Database Systems)
Sensitivity of PCA for traffic anomaly detection
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Learning from Data Streams: Processing Techniques in Sensor Networks
Learning from Data Streams: Processing Techniques in Sensor Networks
Machine learning approaches to network anomaly detection
SYSML'07 Proceedings of the 2nd USENIX workshop on Tackling computer systems problems with machine learning techniques
Disk aware discord discovery: finding unusual time series in terabyte sized datasets
Knowledge and Information Systems
ACM Computing Surveys (CSUR)
The fast recursive row-Householder subspace tracking algorithm
Signal Processing
Projection approximation subspace tracking
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Fast and Stable Subspace Tracking
IEEE Transactions on Signal Processing
Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems
Anomaly localization for network data streams with graph joint sparse PCA
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Rethinking concepts of the dendritic cell algorithm for multiple data stream analysis
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
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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We consider the problem of anomaly detection in multiple co-evolving data streams. In this paper, we introduce FRAHST (Fast Rank-Adaptive row-Householder Subspace Tracking). It automatically learns the principal subspace from N numerical data streams and an anomaly is indicated by a change in the number of latent variables. Our technique provides state-of-the-art estimates for the subspace basis and has a true dominant complexity of only 5Nr operations while satisfying all desirable streaming constraints. FRAHST successfully detects subtle anomalous patterns and when compared against four other anomaly detection techniques, it is the only with a consistent F1 ≥ 80% in the Abilene datasets as well as in the ISP datasets introduced in this work.