Advertisement

Adaptive Agents Applied to Intrusion Detection

  • Javier Carbó
  • Agustín Orfila
  • Arturo Ribagorda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2691)

Abstract

This paper proposes a system of agents that make predictions over the presence of intrusions. Some of the agents act as predictors implementing a given Intrusion Detection model, sniffing out the same traffic. An assessment agent weights the forecasts of such predictor agents, giving a final binary conclusion using a probabilistic model. These weights are continuously adapted according to the previous performance of each predictor agent. Other agent establishes if the prediction from the assessor agent was right or not, sending him back the results. This process is continually repeated and runs without human interaction. The effectiveness of our proposal is measured with the usual method applied in Intrusion Detection domain: Receiver Operating Characteristic curves (detection rate versus false alarm rate). Results of the adaptive agents applied to intrusion detection improve ROC curves as it is shown in this paper.

Keywords

Receiver Operating Characteristic Receiver Operating Characteristic Curve False Alarm Rate Multiagent System Intrusion Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Balasubramaniyan, J. S., Garcia J.O., Isacoff D., Spafford E., Zamboni D.,: An Architecture for Intrusion Detection using Autonomous Agents. Procs. of the 14th Annual Computer Security Applications Conf., pp. 13–24. IEEE Computer Society, December 1998.Google Scholar
  2. [2]
    Vigna G., Cassell B., Fayram D.,: An Intrusion Detection System for Aglets. 6th Int. Conf. on Mobile Agents, Barcelona, Spain, October 2002.Google Scholar
  3. [3]
    Carver C., Hill J., Surdu J., Pooch U.,: A methodology for using Intelligent Agents to provide Automated Intrusion Response. Procs. of the IEEE Systems, Man, and Cybernetics Information Assurance and SecurityWorkshop, West Point, NY, June 6–7, 2000.Google Scholar
  4. [4]
    Dasgupta, D., Brian H.,: Mobile Security Agents for Network Traffic Analysis. Procs. DARPA Information Survivability Conf. adn Exposition II, IEEE Society Press, Anaheim, California, June 2001.Google Scholar
  5. [5]
    Crosbie, M., Spafford G.,: Active Defense of a Computer System using Autonomous Agents. Technical Report No. 95-008, Purdue University, U. S., June 1995.Google Scholar
  6. [6]
    Orfila A., Carbo J., Ribagorda A.: Fuzzy logic on Decision Model for IDS. Procs. IEEE Int. Conf. on Fuzzy Systems, St. Louis, May 2003.Google Scholar
  7. [7]
    Baldwin, J. F.,: A calculus for mass assignment in evidential reasoning. Advances in Dempster-Shafer Theory of Evidence, M. Fedrizzi, J. Kacprzyk, R. R. Yager, eds., John Wiley, 1992.Google Scholar
  8. [8]
    Carbo, J., Molina J.M., Davila, J.,: Trust management through fuzzy reputation. Accepted for Int. Journal of Cooperative Information Systems, to appear.Google Scholar
  9. [9]
    Carbo, J., Molina J.M., Davila J.,: A fuzzy model of reputation in multiagent system. Procs. 5th Int. Conf. on Autonomous Agents, Montreal, June 2001.Google Scholar
  10. [10]
    Smith R.G., David R.,: Frameworks for cooperation in distributed problem solving. IEEE Trans. On Systems, Man and Cybernetics, vol. 11, number 1, pp.61–70, June 1995.CrossRefGoogle Scholar
  11. [11]
    Maes, P.,: Agents that reduce work and information overload. Communications of the ACM, vol. 37, number 7, pp. 31–40, 1994.CrossRefGoogle Scholar
  12. [12]
    Rao, A. S., George., M.P.,: BDI-agents from theory to practice. Procs. 1st Int. Conf. on Multiagent Systems (ICMAS’95), San Francisco, June 1995.Google Scholar
  13. [13]
    Finin, T., McKay R., Fritzson, R., McEntire R.,: KQML: an information and knowledge exchange protocol. Procs. Int. Conf. on Building and Sharing of Very Large-Scale Knowledge Bases, December 1993.Google Scholar
  14. [14]
    Axelsson, S.,: Intrusion-detection systems: A taxonomy and survey. Technical Report 99-15, Department of Computer Engineering, Chalmers University of Technology,SE-41296, Goteborg, Sweden, March 2000.Google Scholar
  15. [15]
    Axelsson, S.,: The base rate fallacy and its implications for the difficulty of intrusion detection. In 6th ACM conference on computer and communications security. Kent Ridge Digital Labs, Singapore, 1–4 November 1999, pp. 1–7Google Scholar
  16. [16]
    Lippman, R.P., Fried, D. J., Graf, I., Haines, J. W., Kendall, K. R., McClung, D., Weber, D., Webster, S.E., Wyshhogrod, D., Cunningham, R.K, Zissman, M.A.: Evaluating Intrusion detection systems: the 1998 DARPA O.-line Intrusion Detection Evaluation. Proceedings of the 2000 DARPA information survivality Conference and Exposition (DISCEX), Vol.2, IEEE Press, January 2000Google Scholar
  17. [17]
    Durst, R., Champion, T., Witten, B., Miller, E., Spagnolo, L.: Testing and evaluating computer intrusion detection systems. Communications of the ACM, 42(7), 1999, pp.53–61CrossRefGoogle Scholar
  18. [18]
    Gomez, J., Dasgupta, D.: Evolving Fuzzy Classifiers for Intrusion Detection. Proceedings of the 2002 IEEE. Workshop on Information Assurance. United States Military Academy, West Point, NY June 2001Google Scholar
  19. [19]
    Swets, J.A: The Relative Operating Characteristic in Psychology. Science, 182,1973,pp. 990–1000CrossRefGoogle Scholar
  20. [20]
    Egan, J.P: Signal detection theory and ROC-analysis. Academic Press, 1975Google Scholar
  21. [21]
    Martin, A., Doddington, G., Kamm, T., Ordowski, M., Przybocki, M.: The DET Curve in Assessment of Detection Task Performance. Proceedings EuroSpeech 4. 1998, pp. 1895–1898.Google Scholar
  22. [22]
    Lippmann, R.P., Shahian, D.M.:Coronary Artery Bypass Risk Prediction Using Neural Networks. Annals of Thoracic Surgery, 63. 1997. pp. 1635–1643.CrossRefGoogle Scholar
  23. [23]
    Stanski, H.R., Wilson, L. J., Burrows, W.R. Survey of common verification methods in meteorology. World Weather Report No. 8. World Meteorological Organization. Geneva.Google Scholar
  24. [24]
    Palmer, T.N., Brankovic, C., and Richardson, D. S. A Probability and Decision-Model Analysis of PROVOSTS easonal Ensemble Integrations. Research Department. Technical Memorandum No.265. Nov 1998.Google Scholar
  25. [25]
    Murphy, A.H. A new vector partition of the probability score. J. Appl. Meteor. 1973.Google Scholar
  26. [26]
    Katz, R.W., Murphy, A.H. Forecast value: prototype decision-making models. In Economic value of weather and climate forecasts. Eds. Cambridge University Press. 1997.Google Scholar
  27. [27]
    Wenke, L., Wei, F., Miller, M., Stolfo. S., Zadok, E. Toward Cost-Sensitive Modeling for Intrusion Detection and Response. North Carolina State University. Computer ScienceGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Javier Carbó
    • 1
  • Agustín Orfila
    • 1
  • Arturo Ribagorda
    • 1
  1. 1.Computer Science DepartmentCarlos III University of MadridMadridSpain

Personalised recommendations