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.
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References
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.
Vigna G., Cassell B., Fayram D.,: An Intrusion Detection System for Aglets. 6th Int. Conf. on Mobile Agents, Barcelona, Spain, October 2002.
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.
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.
Crosbie, M., Spafford G.,: Active Defense of a Computer System using Autonomous Agents. Technical Report No. 95-008, Purdue University, U. S., June 1995.
Orfila A., Carbo J., Ribagorda A.: Fuzzy logic on Decision Model for IDS. Procs. IEEE Int. Conf. on Fuzzy Systems, St. Louis, May 2003.
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.
Carbo, J., Molina J.M., Davila, J.,: Trust management through fuzzy reputation. Accepted for Int. Journal of Cooperative Information Systems, to appear.
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.
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.
Maes, P.,: Agents that reduce work and information overload. Communications of the ACM, vol. 37, number 7, pp. 31–40, 1994.
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.
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.
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.
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. 1–7
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 2000
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.53–61
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 2001
Swets, J.A: The Relative Operating Characteristic in Psychology. Science, 182,1973,pp. 990–1000
Egan, J.P: Signal detection theory and ROC-analysis. Academic Press, 1975
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.
Lippmann, R.P., Shahian, D.M.:Coronary Artery Bypass Risk Prediction Using Neural Networks. Annals of Thoracic Surgery, 63. 1997. pp. 1635–1643.
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.
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.
Murphy, A.H. A new vector partition of the probability score. J. Appl. Meteor. 1973.
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.
Wenke, L., Wei, F., Miller, M., Stolfo. S., Zadok, E. Toward Cost-Sensitive Modeling for Intrusion Detection and Response. North Carolina State University. Computer Science
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Carbó, J., Orfila, A., Ribagorda, A. (2003). Adaptive Agents Applied to Intrusion Detection. In: Mařík, V., Pěchouček, M., Müller, J. (eds) Multi-Agent Systems and Applications III. CEEMAS 2003. Lecture Notes in Computer Science(), vol 2691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45023-8_43
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DOI: https://doi.org/10.1007/3-540-45023-8_43
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