Advertisement

Intrusion Detection Using Disagreement-Based Semi-supervised Learning: Detection Enhancement and False Alarm Reduction

  • Yuxin Meng
  • Lam-for Kwok
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7672)

Abstract

With the development of intrusion detection systems (IDSs), a number of machine learning approaches have been applied to intrusion detection. For a traditional supervised learning algorithm, training examples with ground-truth labels should be given in advance. However, in real applications, the number of labeled examples is limited whereas a lot of unlabeled data is widely available, because labeling data requires a large amount of human efforts and is thus very expensive. To mitigate this issue, several semi-supervised learning algorithms, which aim to label data automatically without human intervention, have been proposed to utilize unlabeled data in improving the performance of IDSs. In this paper, we attempt to apply disagreement-based semi-supervised learning algorithm to anomaly detection. Based on our previous work, we further apply this approach to constructing a false alarm filter and investigate its performance of alarm reduction in a network environment. The experimental results show that the disagreement-based scheme is very effective in detecting intrusions and reducing false alarms by automatically labeling unlabeled data, and that its performance can further be improved by co-working with active learning.

Keywords

Intrusion Detection Semi-Supervised Learning Active Learning False Alarm Reduction Network Security and Performance 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Axelsson, S.: The Base-Rate Fallacy and The Difficulty of Intrusion Detection. ACM Transactions on Information and System Security 3(3), 186–205 (2000)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Scarfone, K., Mell, P.: Guide to Intrusion Detection and Prevention Systems (IDPS), pp. 800–894. NIST Special Publication (2007)Google Scholar
  3. 3.
    Vigna, G., Kemmerer, R.A.: NetSTAT: A Network-based Intrusion Detection Approach. In: Proceedings of the Annual Computer Security Applications Conference (ACSAC), pp. 25–34. IEEE Press, New York (1998)Google Scholar
  4. 4.
    Roesch, M.: Snort: Lightweight Intrusion Detection for Networks. In: Proceedings of the 13th Large Installation System Administration Conference (LISA), pp. 229–238 (1999)Google Scholar
  5. 5.
    Valdes, A., Anderson, D.: Statistical Methods for Computer Usage Anomaly Detection Using NIDES. Technical Report, SRI International (January 1995)Google Scholar
  6. 6.
    Ghosh, A.K., Wanken, J., Charron, F.: Detecting Anomalous and Unknown Intrusions Against Programs. In: Proceedings of the Annual Computer Security Applications Conference (ACSAC), pp. 259–267 (1998)Google Scholar
  7. 7.
    Tombini, E., Debar, H., Me, L., Ducasse, M.: A Serial Combination of Anomaly and Misuse IDSes Applied to HTTP Traffic. In: Proceedings of the Annual Computer Security Applications Conference (ACSAC), pp. 428–437 (December 2004)Google Scholar
  8. 8.
    Zhang, J., Zulkernine, M.: A Hybrid Network Intrusion Detection Technique Using Random Forests. In: Proceedings of the International Conference on Availability, Reliability and Security (ARES), pp. 20–22 (April 2006)Google Scholar
  9. 9.
    Zhou, Z.-H., Li, M.: Tri-training: Exploiting Unlabeled Data Using Three Classifiers. IEEE Transactions on Knowledge and Data Engineering 17(11), 1529–1541 (2005)CrossRefGoogle Scholar
  10. 10.
    Zhou, Z.-H., Li, M.: Semi-Supervised Learning by Disagreement. Knowledge and Information Systems 24(3), 415–439 (2010)CrossRefGoogle Scholar
  11. 11.
    Zhou, Z.-H.: Unlabeled Data and Multiple Views. In: Schwenker, F., Trentin, E. (eds.) PSL 2011. LNCS, vol. 7081, pp. 1–7. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  12. 12.
    McHugh, J.: Testing Intrusion Detection Systems: A Critique of the 1998 and 1999 DARPA Intrusion Detection System Evaluations As Performed by Lincoln Laboratory. ACM Transactions on Information System Security 3(4), 262–294 (2000)CrossRefGoogle Scholar
  13. 13.
    Snort. Homepage, http://www.snort.org/ (accessed on May 25, 2012)
  14. 14.
    Lippmann, R.P., Fried, D.J., Graf, I., Haines, J.W., Kendall, K.R., McClung, D., Weber, D., Webster, S.E., Wyschogrod, D., Cunningham, R.K., Zissman, M.A.: Evaluating Intrusion Detection Systems: the 1998 DARPA Off-Line Intrusion Detection Evaluation. In: Proceedings of DARPA Information Survivability Conference and Exposition, pp. 12–26 (2000)Google Scholar
  15. 15.
    Miller, D.J., Uyar, H.S.: A Mixture of Experts Classifier with Learning based on both Labelled and Unlabelled Data. In: Advances in Neural Information Processing Systems 9, pp. 571–577. MIT Press, Cambridge (1997)Google Scholar
  16. 16.
    Chapelle, O., Zien, A.: Semi-Supervised Learning by Low Density Separation. In: Proceedings of the International Workshop on Artificial Intelligence and Statistics, pp. 57–64 (2005)Google Scholar
  17. 17.
    Belkin, M., Niyogi, P.: Semi-Supervised Learning on Riemannian Manifolds. Machine Learning 56(1-3), 209–239 (2004)CrossRefMATHGoogle Scholar
  18. 18.
    Sommer, R., Paxson, V.: Outside the Closed World: On Using Machine Learning for Network Intrusion Detection. In: IEEE Symposium on Security and Privacy, pp. 305–316 (2010)Google Scholar
  19. 19.
    Shahshahani, B., Landgrebe, D.: The Effect of Unlabeled Samples in Reducing the Small Sample Size Problem and Mitigating the Hughes Phenomenon. IEEE Transactions on Geoscience and Remote Sensing 32(5), 1087–1095 (1994)CrossRefGoogle Scholar
  20. 20.
    Meng, Y., Kwok, L.F.: Adaptive False Alarm Filter Using Machine Learning in Intrusion Detection. In: Proceedings of the International Conference on Intelligent Systems and Knowledge Engineering (ISKE), pp. 573–584. Springer (December 2011)Google Scholar
  21. 21.
    Zhu, X.: Semi-Supervised Learning Literature Survey. Technical Report 1530, Computer Science Department, University of Wisconsin, Madison (2006)Google Scholar
  22. 22.
    Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)MATHGoogle Scholar
  23. 23.
    Blum, A., Chawla, S.: Learning from Labeled and Unlabeled Data Using Graph Mincuts. In: Proceedings of the 18th International Conference on Machine Learning, pp. 19–26 (2001)Google Scholar
  24. 24.
    Lee, W., Stolfo, S.J., Mok, K.W.: A Data Mining Framework for Building Intrusion Detection Models. In: IEEE Symposium on Security and Privacy, pp. 120–132 (1999)Google Scholar
  25. 25.
    Blum, A., Mitchell, T.: Combining Labeled and Unlabeled Data with Co-Training. In: Proceedings of the Annual Conference on Computational Learning Theory, pp. 92–100 (1998)Google Scholar
  26. 26.
    Pietraszek, T.: Using Adaptive Alert Classification to Reduce False Positives in Intrusion Detection. In: Jonsson, E., Valdes, A., Almgren, M. (eds.) RAID 2004. LNCS, vol. 3224, pp. 102–124. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  27. 27.
    Law, K.H., Kwok, L.-F.: IDS False Alarm Filtering Using KNN Classifier. In: Lim, C.H., Yung, M. (eds.) WISA 2004. LNCS, vol. 3325, pp. 114–121. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  28. 28.
    Nigam, K., McCallum, A.K., Thrun, S., Mitchell, T.: Text Classification from Labeled and Unlabeled Documents Using EM. Machine Learning 39(2-3), 103–134 (2000)CrossRefMATHGoogle Scholar
  29. 29.
    Alharby, A., Imai, H.: IDS False Alarm Reduction Using Continuous and Discontinuous Patterns. In: Ioannidis, J., Keromytis, A.D., Yung, M. (eds.) ACNS 2005. LNCS, vol. 3531, pp. 192–205. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  30. 30.
    Lane, T.: A Decision-Theoretic, Semi-Supervised Model for Intrusion Detection. In: Machine Learning and Data Mining for Computer Security: Methods and Applications, pp. 1–19 (2006)Google Scholar
  31. 31.
    Chen, C., Gong, Y., Tian, Y.: Semi-Supervised Learning Methods for Network Intrusion Detection. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 2603–2608 (2008)Google Scholar
  32. 32.
    Panda, M., Patra, M.R.: Semi-Naïve Bayesian Method for Network Intrusion Detection System. In: Leung, C.S., Lee, M., Chan, J.H. (eds.) ICONIP 2009, Part I. LNCS, vol. 5863, pp. 614–621. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  33. 33.
    Mao, C.H., Lee, H.M., Parikh, D., Chen, T., Huang, S.Y.: Semi-Supervised Co-Training and Active Learning based Approach for Multi-View Intrusion Detection. In: Proceedings of the 2009 ACM Symposium on Applied Computing (SAC), pp. 2042–2048 (2009)Google Scholar
  34. 34.
    Zhang, M., Mei, H.: A New Method for Filtering IDS False Positives with Semi-supervised Classification. In: Huang, D.-S., Jiang, C., Bevilacqua, V., Figueroa, J.C. (eds.) ICIC 2012. LNCS, vol. 7389, pp. 513–519. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  35. 35.
    Chiu, C.-Y., Lee, Y.-J., Chang, C.-C., Luo, W.-Y., Huang, H.-C.: Semi-Supervised Learning for False Alarm Reduction. In: Proceedings of the 10th IEEE International Conference on Data Mining (ICDM), pp. 595–605 (2010)Google Scholar
  36. 36.
    Caruana, R., Niculescu-Mizil, A., Crew, G., Ksikes, A.: Ensemble Selection from Libraries of Models. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 137–144 (2004)Google Scholar
  37. 37.
    Nigam, K., Ghani, R.: Analyzing the Effectiveness and Applicability of Co-Training. In: Proceedings of the 9th ACM International Conference on Information and Knowledge Management, pp. 86–93 (2000)Google Scholar
  38. 38.
    Almgren, M., Jonsson, E.: Using Active Learning in Intrusion Detection. In: Proceedings of the IEEE Computer Security Foundations Workshop (CSFW), pp. 88–98 (2004)Google Scholar
  39. 39.
    Görnitz, N., Kloft, M., Rieck, K., Brefeld, U.: Active Learning for Network Intrusion Detection. In: Proceedings of the ACM Workshop on Security and Artificial Intelligence (AISec), pp. 47–54 (2009)Google Scholar
  40. 40.
    WEKA - Waikato Environment for Knowledge Analysis, http://www.cs.waikato.ac.nz/ml/weka/ (accessed on May 20, 2012)
  41. 41.
    Wireshark, Homepage, http://www.wireshark.org (accessed on April 10, 2012)

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yuxin Meng
    • 1
  • Lam-for Kwok
    • 1
  1. 1.Department of Computer ScienceCity University of Hong KongHong KongChina

Personalised recommendations