Abstract
This paper employs SPOT (Stream Projected Outlier deTector) 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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aggarwal, C.C., Yu, P.S.: Outlier Detection for High Dimensional Data. In: SIGMOD 2001 (2001)
Forrest, S.A., Hofmeyr, S.A., Somayaji, A., Longstaff, T.A.: A Sense of Self for UNIX Processes. In: Proceedings of the 1996 IEEE Symposium on Security and Privacy, pp. 120–128 (2001)
Forrest, S.A., Warrender, C., Perlmutter, B.: Detecting Intrusions Using System Calls: Alternative Data Models. In: Proceedings of the 1999 IEEE Computer Society Symposium on Research in Security and Privacy, pp. 133–145 (1999)
Garcia-Teodoro, P., Diaz-Verdejo, J., Macia-Fernandez, G., Vazquez, E.: Anomaly-Based Network Intrusion Detection: Techniques, Systems and Challenges. Computers and Security 28, 18–28 (2009)
Symantec Global Internet Security Threat Report: Trends for 2008, Vol. XIV (2008) (published April 2009)
Tan, K., Maxion, R.: Why 6? Defining the Operational Limits of Stide,and Anomaly-Based Intrusion Detectors. In: Proceedings of the 1996 IEEE Symposium on Security and Privacy, pp. 133–145 (2002)
Wenke, J., Salvatore, J.: Framework for Construction Features and Models for Intrusion Detection Systems. In: TISSEC, pp. 227–261 (2000)
Zhang, J.: Towards Outlier Detection for High-Dimensional Data Streams Using Projected Outlier Analysis Strategy. PHD Thesis. Dalhousie University (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kershaw, D., Gao, Q., Wang, H. (2011). Anomaly-Based Network Intrusion Detection Using Outlier Subspace Analysis: A Case Study. In: Butz, C., Lingras, P. (eds) Advances in Artificial Intelligence. Canadian AI 2011. Lecture Notes in Computer Science(), vol 6657. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21043-3_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-21043-3_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21042-6
Online ISBN: 978-3-642-21043-3
eBook Packages: Computer ScienceComputer Science (R0)