Summary
The paper considers one of the topmost security related problems that is situation assessment. Specific classification and data mining issues associated with this task and methods of their solution are the subjects of the paper. In particular, the paper discusses situation assessment data model specifying situation, approach to learning of situation assessment, generic architecture of multi-agent situation assessment systems and software engineering issues. Detection of abnormal use of computer network is a case study used for demonstration of the main research results.
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Ben-Bassat, M., Freedy, A.: Knowledge Requirements and Management in Expert Decision Support Systems for (Military) Situation Assessment. IEEE Transactions on Systems, Man and Cybernetics, vol.12. (2002) pp. 479–490
Cohen, W.: Fast efficient rule induction. Machine Learning: 12th International Conference, CA, Morgan Kaufmann (1995)
Goodman, I., Mahler, R., and Nguen, H.: Mathematics of Data Fusion. Kluwer Academic Publishers, (1997)
Gorodetsky, V., Karsaeyv, O., and Samoilov, V.: Software Tool for Agent-Based Distributed Data Mining. Proceedings of the IEEE Conference “Knowledge Intensive Multi-agent Systems” (KIMAS 03), Boston, USA (2003)
Gorodetski, V., Karsaev, O., Kotenko I., and Khabalov, A.: Software Development Kit for Multi-agent Systems Design and Implementation. In B. Dunin-Keplicz, E. Navareski (Eds.), From Theory to Practice in Multi-agent Systems. Lecture Notes in Artificial Intelligence, Vol. 2296, (2002) 121–130
Gorodetsky, V., Karsaev, O.: Mining of Data with Missing Values: A Lattice-based Approach. In Proceedings of International Workshop on the Foundation of Data Mining and Discovery, Japan, (2002) 151–156
Gorodetsky, V, Karsaev, O.: Algorithm of Rule Extraction from Learning Data. Proceedings of the 8-th International Conference “Expert Systems & Artificial Intelligence” (EXPERSYS-96) (1996) 133–138
Greeenhill, S., Venkatesh, S., Pearce, A., Ly, T.C.: Representations and Processes in Decision Modeling. DSTO Aeronautical and Maritime Research Laboratory, Australia, DSTO-GD-0318 (2002)
Michalski, R.: A Theory and Methodology of Inductive Learning. Machine Learning, vol.1, Carbonel, J.G., Michalski, R.S. and Mitchel, T.M. (Eds.). Tigoda, Palo Alto (1983) 83–134
Michalski, R. and Kaufman, A.: Data Mining and Knowledge Discovery: A Review of Issues and Multistrategy Approach. Machine learning and Data Mining: Methods and Applications, John Wiley and Sons, (1997)
Proceeding of the Fifth International Conference on Information Fusion (IF-2002). Annapolis, MD, July 7–11, (2002)
Proceeding of the Six International Conference on Information Fusion (IF-2003). Melbourne, Australia, July 13–17 (2003)
Salerno, J., Hinman, M., Boulware, D.: Building a Framework for Situation Assessment. Proceedings of The 7th International Conference on Information Fusion. Sweden (2004)
Salerno, J.: Information Fusion: A High-level Architecture Overview. In CD Proceedings of the Fusion-2002, Annapolis, MD (2002) 680–686.
Than, C. L., Greenhill, S., Venkatesh, S., Pearce, A.: Multiple Hypotheses Situation Assessment. Proceedings of The 6th International Conference on Information Fusion. Australia, (2003) 972–978
Wooldridge, M., Jennings, N.R., Kinny, D.: The Gaia Methodology for Agent-Oriented Analysis and Design. Journal of Autonomous Agents and Multi-Agent Systems, 3, vol.3. (2000) 285–312
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Gorodetsky, V., Karsaev, O., Samoilov, V. (2005). Multi-agent and Data Mining Technologies for Situation Assessment in Security-related Applications. In: Monitoring, Security, and Rescue Techniques in Multiagent Systems. Advances in Soft Computing, vol 28. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32370-8_32
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DOI: https://doi.org/10.1007/3-540-32370-8_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23245-2
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