Anomaly-based network intrusion detection using outlier subspace analysis: a case study

Authors:
David Kershaw;Qigang Gao;Hai Wang
Affiliations:
Faculty of Computer Science, Dalhousie University;Faculty of Computer Science, Dalhousie University;Sobey School of Business, St. Mary's University
Venue:
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
Year:
2011

Citing 5
Cited 1

Outlier detection for high dimensional data

SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
A framework for constructing features and models for intrusion detection systems

ACM Transactions on Information and System Security (TISSEC)
"Why 6?" Defining the Operational Limits of Stide, an Anomaly-Based Intrusion Detector

SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
Towards outlier detection for high-dimensional data streams using projected outlier analysis strategy

Towards outlier detection for high-dimensional data streams using projected outlier analysis strategy
A sense of self for Unix processes

SP'96 Proceedings of the 1996 IEEE conference on Security and privacy

Adaptive ROC-based ensembles of HMMs applied to anomaly detection

Pattern Recognition

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Abstract

This paper employs SPOT (Stream Projected Outlier de-Tector) as a prototype system for anomaly-based intrusion detection and evaluates its performance against other major methods. SPOT is capable of processing high-dimensional data streams and detecting novel attacks which exhibit abnormal behavior, making it a good candidate for network intrusion detection. This paper demonstrates SPOT is effective to distinguish between normal and abnormal processes in a UNIX System Call dataset.