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

D 2013

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

STEHLÍK, Martin, Adam SALEH, Andriy STETSKO and Václav MATYÁŠ

Basic information

Original name

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

Authors

STEHLÍK, Martin (203 Czech Republic, guarantor, belonging to the institution), Adam SALEH (703 Slovakia, belonging to the institution), Andriy STETSKO (804 Ukraine, belonging to the institution) and Václav MATYÁŠ (203 Czech Republic, belonging to the institution)

Edition

Cambridge, MA 02142-1493 USA, Advances in Artificial Life, ECAL 2013, Proceedings of the Twelfth European Conference on the Synthesis and Simulation of Living Systems, p. 569-576, 8 pp. 2013

Publisher

MIT Press

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

URL

RIV identification code

RIV/00216224:14330/13:00066312

Organization unit

Faculty of Informatics

ISBN

978-0-262-31709-2

DOI

http://dx.doi.org/10.7551/978-0-262-31709-2-ch082

Keywords in English

Evolutionary algorithm; Multi-objective evolutionary algorithm; Optimization; Wireless sensor network; Intrusion detection system

Abstract

V originále

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.

Links

GAP202/11/0422, research and development project

Name: Bezpečnostní protokoly podporující soukromí a detekce průniku v bezdrátových senzorových sítích (Acronym: P202/11/0422)

Investor: Czech Science Foundation

Citovat

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. 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.

@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.

Detailed Information on Publication Record