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@inproceedings{1121728,
author = {Stehlík, Martin and Saleh, Adam and Stetsko, Andriy and Matyáš, Václav},
address = {Cambridge, MA 02142-1493 USA},
booktitle = {Advances in Artificial Life, ECAL 2013, Proceedings of the Twelfth European Conference on the Synthesis and Simulation of Living Systems},
doi = {http://dx.doi.org/10.7551/978-0-262-31709-2-ch082},
editor = {Pietro Liò, Orazio Miglino, Giuseppe Nicosia, Stefano Nolfi and Mario Pavone},
keywords = {Evolutionary algorithm; Multi-objective evolutionary algorithm; Optimization; Wireless sensor network; Intrusion detection system},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Cambridge, MA 02142-1493 USA},
isbn = {978-0-262-31709-2},
pages = {569-576},
publisher = {MIT Press},
title = {Multi-Objective Optimization of Intrusion Detection Systems for Wireless Sensor Networks},
url = {http://mitpress.mit.edu/sites/default/files/titles/content/ecal13/ch082.html},
year = {2013}
}
TY - JOUR
ID - 1121728
AU - Stehlík, Martin - Saleh, Adam - Stetsko, Andriy - Matyáš, Václav
PY - 2013
TI - Multi-Objective Optimization of Intrusion Detection Systems for Wireless Sensor Networks
PB - MIT Press
CY - Cambridge, MA 02142-1493 USA
SN - 9780262317092
KW - Evolutionary algorithm
KW - Multi-objective evolutionary algorithm
KW - Optimization
KW - Wireless sensor network
KW - Intrusion detection system
UR - http://mitpress.mit.edu/sites/default/files/titles/content/ecal13/ch082.html
L2 - http://mitpress.mit.edu/sites/default/files/titles/content/ecal13/ch082.html
N2 - 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.
ER -
STEHLÍK, Martin, Adam SALEH, Andriy STETSKO and Václav MATYÁŠ. Multi-Objective Optimization of Intrusion Detection Systems for Wireless Sensor Networks. Online. In Pietro Liò, Orazio Miglino, Giuseppe Nicosia, Stefano Nolfi and Mario Pavone. \textit{Advances in Artificial Life, ECAL 2013, Proceedings of the Twelfth European Conference on the Synthesis and Simulation of Living Systems}. Cambridge, MA 02142-1493 USA: MIT Press, 2013, p.~569-576. ISBN~978-0-262-31709-2. Available from: https://dx.doi.org/10.7551/978-0-262-31709-2-ch082.
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