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Using Boosting Learning Method for Intrusion Detection

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Advanced Data Mining and Applications (ADMA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3584))

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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.

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References

  1. Illgun, K., Kemmerer, R., Philips, A.: State Transition Analysis: A Rule-based Intrusion Detection Approach. IEEE Transaction on Software Engineering 2, 181–199 (1995)

    Article  Google Scholar 

  2. 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)

    Google Scholar 

  3. 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)

    Google Scholar 

  4. William, W.C.: Efficient Rule Induction. In: Proceedings of the 12th International Conference on Machine Learning. Morgan Kaufmann, San Francisco (1995)

    Google Scholar 

  5. Schapire, R.E.: The Strength of Weak Learnability. Machine Learning 2, 197–227 (1990)

    Google Scholar 

  6. 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)

    Article  Google Scholar 

  7. Quinlan, J.R.: Bagging, Boosting and C4.5. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence, Menlo Park, pp. 725–730 (1996)

    Google Scholar 

  8. 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)

    Chapter  Google Scholar 

  9. KDD CUP 1999 (1999), http://kdd.ics.uci.edu/database/kddcup99/kddcup99.html

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© 2005 Springer-Verlag Berlin Heidelberg

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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

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  • 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)

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