Abstract
This paper investigates how intrusion detection system (IDS) sensors should best be placed on a network when there are several competing evaluation criteria. This is a computationally difficult problem and we show how Multi-Objective Genetic Algorithms provide an excellent means of searching for optimal placements.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)
Coello, C.A.C., Nacional, L.: An updated survey of ga-based multiobjective optimization techniques. ACM Computing Surveys 32, 109–143 (1998)
Lu, W., Traore, I.: Detecting new forms of network intrusion using genetic programming. In: Proceedings of the 2003 Congress on Evolutionary Computation (2003)
Noel, S., Jajodia, S.: Attack graphs for sensor placement, alert prioritization, and attack response. In: Cyberspace Research Workshop (2007)
Rolando, M., Rossi, M., Sanarico, N., Mandrioli, D.: A formal approach to sensor placement and configuration in a network intrusion detection system. In: SESS 2006: Proceedings of the 2006 international workshop on Software engineering for secure systems, pp. 65–71. ACM, New York (2006)
Issariyakul, T., Hossain, E.: An Introduction to Network Simulator Ns2. Springer, Heidelberg (2008)
Shaikh, S.A., Chivers, H., Nobles, P., Clark, J.A., Chen, H.: Network reconnaissance. Network Security 11, 12–16 (2008)
Gu, G., Fogla, P., Dagon, D., Lee, W., Skoric, B.: Measuring intrusion detection capability: an information-theoretic approach. In: ASIACCS 2006: Proceedings of the 2006 ACM Symposium on Information, computer and communications security, pp. 90–101. ACM, New York (2006)
Luke, S.: A java-based evolutionary computation research system (2008), http://cs.gmu.edu/~eclab/projects/ecj/
Zitzler, E., Laumanns, M., Thiele, L.: Spea2: Improving the strength pareto evolutionary algorithm. Technical Report 103, Swiss Federal Institute of Technology (2001)
Shaikh, S.A., Chivers, H., Nobles, P., Clark, J.A., Chen, H.: A deployment value model for intrusion detection sensors. In: 3rd International Conference on Information Security and Assurance. LNCS, vol. 5576, pp. 250–259. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, H., Clark, J.A., Tapiador, J.E., Shaikh, S.A., Chivers, H., Nobles, P. (2009). A Multi-objective Optimisation Approach to IDS Sensor Placement. In: Herrero, Á., Gastaldo, P., Zunino, R., Corchado, E. (eds) Computational Intelligence in Security for Information Systems. Advances in Intelligent and Soft Computing, vol 63. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04091-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-04091-7_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04090-0
Online ISBN: 978-3-642-04091-7
eBook Packages: EngineeringEngineering (R0)