Multi-Objective Optimization of Intrusion Detection Systems for Wireless Sensor Networks

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Authors

STEHLÍK Martin SALEH Adam STETSKO Andriy MATYÁŠ Václav

Year of publication 2013
Type Article in Proceedings
Conference Advances in Artificial Life, ECAL 2013, Proceedings of the Twelfth European Conference on the Synthesis and Simulation of Living Systems
MU Faculty or unit

Faculty of Informatics

Citation
Web http://mitpress.mit.edu/sites/default/files/titles/content/ecal13/ch082.html
Doi http://dx.doi.org/10.7551/978-0-262-31709-2-ch082
Field Informatics
Keywords Evolutionary algorithm; Multi-objective evolutionary algorithm; Optimization; Wireless sensor network; Intrusion detection system
Description Intrusion detection is an essential mechanism to protect wireless sensor networks against internal attacks that are relatively easy and not expensive to mount in these networks. Recently, we proposed, implemented and tested a framework that helps a network operator to find a trade-off between detection accuracy and usage of resources that are usually highly constrained in wireless sensor networks. We used a single-objective optimization evolutionary algorithm for this purpose. This approach, however, has its limitations. In order to eliminate them, we show benefits of multi-objective evolutionary algorithms for intrusion detection parametrization and examine two multi-objective evolutionary algorithms (NSGA-II and SPEA2). Our examination focuses on the impact of an evolutionary algorithm (and its parameters) on the optimality of found solutions, the speed of convergence and the number of evaluations.
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