M-Kernel Merging: Towards Density Estimation over Data Streams
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
Diagnosing network-wide traffic anomalies
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
A Classification Framework for Anomaly Detection
The Journal of Machine Learning Research
Mining anomalies using traffic feature distributions
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Kernel estimation of density level sets
Journal of Multivariate Analysis
Detection and identification of network anomalies using sketch subspaces
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Impact of packet sampling on anomaly detection metrics
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Machine learning approaches to network anomaly detection
SYSML'07 Proceedings of the 2nd USENIX workshop on Tackling computer systems problems with machine learning techniques
The kernel recursive least-squares algorithm
IEEE Transactions on Signal Processing
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Large backbone networks are regularly affected by a range of anomalies. This paper presents an online anomaly detection algorithm based on Kernel Density Estimates. The proposed algorithm sequentially and adaptively learns the definition of normality in the given application, assumes no prior knowledge regarding the underlying distributions, and then detects anomalies subject to a user-set tolerance level for false alarms. Comparison with the existing methods of Geometric Entropy Minimization, Principal Component Analysis and One-Class Neighbor Machine demonstrates that the proposed method achieves superior performance with lower complexity.