Abstract
It is an important research topic to improve detection rate and reduce false positive rate of detection model in the field of intrusion detection. This paper adopts an improved boosting method to enhance generalization performance of intrusion detection model based on rule learning algorithm, and presents a boosting intrusion detection rule learning algorithm (BIDRLA). The experiment results on the standard intrusion detection dataset validate the effectiveness of BIDRLA.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Illgun, K., Kemmerer, R., Philips, A.: State Transition Analysis: A Rule-based Intrusion Detection Approach. IEEE Transaction on Software Engineering 2, 181–199 (1995)
Karlton, S., Mohammed, Z.: ADMIT: Anomaly-based Data Mining for Intrusions. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 386–395. ACM Press, Edmonton Alberta Canada (2002)
Wenke, L., Stolfo, S.J., Mok, K.W.: A Data Mining Framework for Building Intrusion Detection Models. In: Proceedings of the 1999 IEEE Symposium on Security and Privacy, pp. 120–132. IEEE Press, Oakland (1999)
William, W.C.: Efficient Rule Induction. In: Proceedings of the 12th International Conference on Machine Learning. Morgan Kaufmann, San Francisco (1995)
Schapire, R.E.: The Strength of Weak Learnability. Machine Learning 2, 197–227 (1990)
Freund, Y., Robert, E.S.: A Decision-Theoretic Generalization of On-line Learning and an Application to Boosting. Journal of Computer and System Sciences 1, 119–139 (1997)
Quinlan, J.R.: Bagging, Boosting and C4.5. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence, Menlo Park, pp. 725–730 (1996)
Schapire, R.E., Singer, Y.: Improved Boosting Algorithms Using Confidence-rated Predictions. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 80–91. ACM Press, Madison Wisconsin United States (1998)
KDD CUP 1999 (1999), http://kdd.ics.uci.edu/database/kddcup99/kddcup99.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, W., Yun, XC., Yang, YT. (2005). Using Boosting Learning Method for Intrusion Detection. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_75
Download citation
DOI: https://doi.org/10.1007/11527503_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27894-8
Online ISBN: 978-3-540-31877-4
eBook Packages: Computer ScienceComputer Science (R0)