Attack Detection Using Evolutionary Computation

C 2017

Attack Detection Using Evolutionary Computation

STEHLÍK, Martin, Václav MATYÁŠ a Andriy STETSKO

Základní údaje

Originální název

Attack Detection Using Evolutionary Computation

Autoři

STEHLÍK, Martin (203 Česká republika, domácí), Václav MATYÁŠ (203 Česká republika, garant, domácí) a Andriy STETSKO (203 Česká republika, domácí)

Vydání

Germany, Computational Intelligence in Wireless Sensor Networks, od s. 99-129, 31 s. Studies in Computational Intelligence, 2017

Nakladatel

Springer International Publishing

Další údaje

Jazyk

angličtina

Typ výsledku

Kapitola resp. kapitoly v odborné knize

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Německo

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Kód RIV

RIV/00216224:14330/17:00094468

Organizační jednotka

Fakulta informatiky

ISBN

978-3-319-47713-8

DOI

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

UT WoS

000413356100006

Klíčová slova anglicky

wireless sensor network; attack detection; evolutionary computation

Štítky

topvydavatel

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 13. 5. 2020 23:17, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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.

Návaznosti

VG20102014031, projekt VaV

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

Investor: Ministerstvo vnitra ČR, Experimentální vývoj bezpečnostní softwarové platformy se systémem detekce průniku a režimy ochrany soukromí pro bezdrátové senzorové sítě

Citovat

STEHLÍK, Martin, Václav MATYÁŠ a 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, s. 99-129. Studies in Computational Intelligence. ISBN 978-3-319-47713-8. Dostupné z: https://dx.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 = {http://dx.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 a 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, s.~99-129. Studies in Computational Intelligence. ISBN~978-3-319-47713-8. Dostupné z: https://dx.doi.org/10.1007/978-3-319-47715-2\_{}5.

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