A Multi-objective Optimisation Approach to IDS Sensor Placement

Author:

Chen Hao,Clark John A.,Tapiador Juan E.,Shaikh Siraj A.,Chivers Howard,Nobles Philip

Publisher

Springer Berlin Heidelberg

Reference11 articles.

1. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)

2. Coello, C.A.C., Nacional, L.: An updated survey of ga-based multiobjective optimization techniques. ACM Computing Surveys 32, 109–143 (1998)

3. Lu, W., Traore, I.: Detecting new forms of network intrusion using genetic programming. In: Proceedings of the 2003 Congress on Evolutionary Computation (2003)

4. Noel, S., Jajodia, S.: Attack graphs for sensor placement, alert prioritization, and attack response. In: Cyberspace Research Workshop (2007)

5. 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)

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1. Neural Network Approximation of Simulation-based IDS Fitness Evaluation;2022 IEEE 25th International Conference on Computational Science and Engineering (CSE);2022-12

2. On Optimal Configuration of IDS for RPL Resource-Constrained Networks Using Evolutionary Algorithm;Proceedings of the Future Technologies Conference (FTC) 2022, Volume 2;2022-10-13

3. An elastic intrusion detection system for software networks;Annals of Telecommunications;2016-03-05

4. Evolving a Trust Model for Peer-to-Peer Networks Using Genetic Programming;Applications of Evolutionary Computation;2014

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