Summary
Data Mining Techniques have been successfully applied in many different fields including marketing, manufacturing, fraud detection and network management. Over the past years there is a lot of interest in security technologies such as intrusion detection, cryptography, authentication and firewalls. This chapter discusses the application of Data Mining techniques to computer security. Conclusions are drawn and directions for future research are suggested.
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.
References
Barbara D., Wu N., and Jajodia S., Detecting novel network intrusions using bayes estimators. In Proc. First SIAM Conference on Data Mining, Chicago, IL, April 2001.
Barbara D., Couto J., Jajodia S., and Wu N., Adam: Detecting Intrusions by Data Mining, In Proc. 2nd Annual IEEE Information Assurance Workshop, West Point, NY, June 2001.
Cuppens F. and Miege A., Alert Correlation in a Cooperative Intrusion Detection Framework, Proc. IEEE Symposium on Security and Privacy, May 2002.
Ertoz L., Eilertson E., Lazarevic A., Tan P., Dokes P., Kumar V., Srivastava J., Detection ofNovel Attacks using Data Mining, Proc. IEEEWorkshop on Data Mining and Computer Security, November 2003.
Han J. and Kamber M., Data Mining: Concepts and Techniques, Morgan Kaufmann, August 2000.
Kumar V., Lazarevic A., Ertoz L., Ozgur A., Srivastava J., A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection, In Proc. Third SIAM International Conference on Data Mining, San Francisco, May 2003.
Lee W., Stolfo, S. J., and Kwok K. W. Mining audit data to build intrusion detection models. In Proc. Fourth International Conference on Knowledge Discovery and Data Mining, NewYork, 1998.
Lee W. and Stolfo S. J. Data Mining approaches for intrusion detection, In Proc. Seventh USENIX Security Symposium, San Antonio, TX, 1998.
Ning P., Cui Y., Reeves D. S., Constructing Attack Scenarios through Correlation of Intrusion Alerts, Proc. ACM Computer and Communications Security Conf., 2002.
Ning P., Xu D., earning Attack Strategies from Intrusion Alerts, Proc. ACM Computer and Communications Security Conf., 2003.
Portnoy L., Eskin E., Stolfo S. J., Intrusion Detection with unlabeled data using clustering. In Proceedings of ACM Workshop on Data Mining Applied to Security, 2001.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Singhal, A., Jajodia, S. (2009). Data Mining for Intrusion Detection. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09823-4_61
Download citation
DOI: https://doi.org/10.1007/978-0-387-09823-4_61
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-09822-7
Online ISBN: 978-0-387-09823-4
eBook Packages: Computer ScienceComputer Science (R0)