Attack Detection Using Evolutionary Computation

C 2017

Attack Detection Using Evolutionary Computation

STEHLÍK, Martin; Václav MATYÁŠ and Andriy STETSKO

Basic information

Original name

Attack Detection Using Evolutionary Computation

Authors

STEHLÍK, Martin (203 Czech Republic, belonging to the institution); Václav MATYÁŠ (203 Czech Republic, guarantor, belonging to the institution) and Andriy STETSKO (203 Czech Republic, belonging to the institution)

Edition

Germany, Computational Intelligence in Wireless Sensor Networks, p. 99-129, 31 pp. Studies in Computational Intelligence, 2017

Publisher

Springer International Publishing

Other information

Language

English

Type of outcome

Chapter(s) of a specialized book

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Germany

Confidentiality degree

is not subject to a state or trade secret

Publication form

printed version "print"

RIV identification code

RIV/00216224:14330/17:00094468

Organization unit

Faculty of Informatics

ISBN

978-3-319-47713-8

DOI

https://doi.org/10.1007/978-3-319-47715-2_5

UT WoS

000413356100006

EID Scopus

2-s2.0-85010333254

Keywords in English

wireless sensor network; attack detection; evolutionary computation

Abstract

In the original language

Wireless sensor networks (WSNs) are often deployed in open and potentially hostile environments. An attacker can easily capture the sensor nodes or replace them with malicious devices that actively manipulate the communication. Several intrusion detection systems (IDSs) have been proposed to detect different kinds of active attacks by sensor nodes themselves. However, the optimization of the IDSs w.r.t. the accuracy and also sensor nodes’ resource consumption is often left unresolved. We use multi-objective evolutionary algorithms to optimize the IDS with respect to three objectives for each specific WSN application and environment. The optimization on two detection techniques aimed at a selective forwarding attack and a delay attack is evaluated. Moreover, we discuss various attacker strategies ranging from an attacker behavior to a deployment of the malicious sensor nodes in the WSN. The robustness of the IDS settings optimized for six different attacker strategies is evaluated.

Links

VG20102014031, research and development project

Name: Experimentální vývoj bezpečnostní softwarové platformy se systémem detekce průniku a režimy ochrany soukromí pro bezdrátové senzorové sítě (Acronym: WSNSec)

Investor: Ministry of the Interior of the CR

Cite

STEHLÍK, Martin; Václav MATYÁŠ and Andriy STETSKO. Attack Detection Using Evolutionary Computation. In Abraham, Ajith, Falcon, Rafael, Koeppen, Mario. Computational Intelligence in Wireless Sensor Networks. Germany: Springer International Publishing, 2017, p. 99-129. Studies in Computational Intelligence. ISBN 978-3-319-47713-8. Available from: https://doi.org/10.1007/978-3-319-47715-2_5.

@inbook{1368341,
   author = {Stehlík, Martin and Matyáš, Václav and Stetsko, Andriy},
   address = {Germany},
   booktitle = {Computational Intelligence in Wireless Sensor Networks},
   doi = {https://doi.org/10.1007/978-3-319-47715-2_5},
   editor = {Abraham, Ajith, Falcon, Rafael, Koeppen, Mario},
   keywords = {wireless sensor network; attack detection; evolutionary computation},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Germany},
   isbn = {978-3-319-47713-8},
   pages = {99-129},
   publisher = {Springer International Publishing},
   title = {Attack Detection Using Evolutionary Computation},
   year = {2017}
}

TY  - CHAP
ID  - 1368341
AU  - Stehlík, Martin - Matyáš, Václav - Stetsko, Andriy
PY  - 2017
TI  - Attack Detection Using Evolutionary Computation
VL  - Studies in Computational Intelligence
PB  - Springer International Publishing
CY  - Germany
SN  - 9783319477138
KW  - wireless sensor network
KW  - attack detection
KW  - evolutionary computation
N2  - Wireless sensor networks (WSNs) are often deployed in open and potentially hostile environments. An attacker can easily capture the sensor nodes or replace them with malicious devices that actively manipulate the communication. Several intrusion detection systems (IDSs) have been proposed to detect different kinds of active attacks by sensor nodes themselves. However, the optimization of the IDSs w.r.t. the accuracy and also sensor nodes’ resource consumption is often left unresolved. We use multi-objective evolutionary algorithms to optimize the IDS with respect to three objectives for each specific WSN application and environment. The optimization on two detection techniques aimed at a selective forwarding attack and a delay attack is evaluated. Moreover, we discuss various attacker strategies ranging from an attacker behavior to a deployment of the malicious sensor nodes in the WSN. The robustness of the IDS settings optimized for six different attacker strategies is evaluated.
ER  -

STEHLÍK, Martin; Václav MATYÁŠ and Andriy STETSKO. Attack Detection Using Evolutionary Computation. In Abraham, Ajith, Falcon, Rafael, Koeppen, Mario. \textit{Computational Intelligence in Wireless Sensor Networks}. Germany: Springer International Publishing, 2017, p.~99-129. Studies in Computational Intelligence. ISBN~978-3-319-47713-8. Available from: https://doi.org/10.1007/978-3-319-47715-2\_{}5.

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