Abstract
Intrusion Detection is one of the crucial real-time problems in the field of computer networking. With the changing technology and the exponential growth of Internet traffic, it is becoming difficult for any existing intrusion detection system to offer a reliable service. From earlier research, we have found that there exists a behavioral pattern in the attacks that can be learned. That is why an Artificial Neural Network is so successful in detecting network intrusions. Still, this approach is not effective in a dynamic environment where changes take place frequently. This paper proposes a blackboard-based Learning Intrusion Detection System, which is controlled by autonomous agents and has an online learning capability. This feature enables the system to adapt itself with the changing environment and to perform better than present systems.
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References
Raytheom, The Insider Threat, White Paper of SilentRunner, Inc. published in 01/09/02.
Lane, T and Brodley C E, Temporal Sequence Learning and Data Reduction for Anomaly Detection, In ACM Transaction on Information and System Security, Vol. 2, No. 3, August 1999.
Lee, W, Stolfo S and Mok K, Adaptive Intrusion Detection: A Data Mining Approach, In Artificial Intelligence Review, Kluwer Academic Publishers, 14(6): 533–567, December 2000.
Denning D E, An Intrusion-Detection Model, In IEEE Transaction on Software Engineering, Vol. Se-13, No. 2, February 1987, 222–232.
Dasgupta D., Gonzalez F., K. Yallapu, Gomez, J., Yarramsettii, R., Dunlap, G. and Greveas, M., CIDS: An Agent-based Intrusion Detection System, In CS Technical Report No. CS-02-001., Feb, 2002.
Balasubramaniyan, J, Fernandez J O, Isacoff D, Spafford E. and Zamboni D, An Architecture for Intrusion Detection Using Autonomous Agents, In COAST Technical Report 98/5, Purdue University, June 1998.
Cannady, J., Artificial Neural Network of Misuse Detection, in the Proceedings of the 1998 National Information Systems Security Conference (NISSC’98) October 5–8 1998. Arlington, VA
Ludovic, ME, GASSATA, a Genetic Algorithm as an Alternative Tool for Security Trails Analysis, in the Proceedings of First International Symposium of Recent Advances in Intrusion Detection, 1998.
Anderson, D., Frivold, T. and Valdes, A Next-generation Intrusion Detection Expert System (NIDES): A Summary, SRI International Technical Report SRI-CSL-95-07, May, 1995.
Sebring, M., Shellhouse, E., Hanna, M. and Whitehurst, R., Expert Systems in Intrusion Detection: A Case Study, In Proceedings of the 11th National Computer Security Conference. 1988.
Weiss S., Kulikowski C., Computer System That Learn, Morgan Kauffman, California, 1991.
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Dass, M., Cannady, J., Potter, W.D. (2003). A Blackboard-Based Learning Intrusion Detection System: A New Approach. In: Chung, P.W.H., Hinde, C., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2003. Lecture Notes in Computer Science(), vol 2718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45034-3_39
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DOI: https://doi.org/10.1007/3-540-45034-3_39
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