Evolutionary fuzzy rules for intrusion detection in wireless sensor networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10247282" target="_blank" >RIV/61989100:27240/21:10247282 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-57796-4_15" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-57796-4_15</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-57796-4_15" target="_blank" >10.1007/978-3-030-57796-4_15</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evolutionary fuzzy rules for intrusion detection in wireless sensor networks

Popis výsledku v původním jazyce

Next-generation digital services and applications often rely on large numbers of devices connected to a common communication backbone. The security of such massively distributed systems is a major issue and advanced methods to improve their ability to detect and counter cybernetic attacks are needed. Evolutionary algorithms can automatically evolve and optimize sophisticated intrusion detection models, suitable for different applications. In this work, a hybrid evolutionary-fuzzy classification and regression algorithm is used to evolve detectors for several types of intrusions in a wireless sensor network. The ability of genetic programming and differential evolution to construct and optimize intrusion detectors for wireless sensor networks is evaluated on a recent intrusion detection data set capturing malicious activity in a wireless sensor network. (C) Springer Nature Switzerland AG 2021.

Název v anglickém jazyce

Evolutionary fuzzy rules for intrusion detection in wireless sensor networks

Popis výsledku anglicky

Next-generation digital services and applications often rely on large numbers of devices connected to a common communication backbone. The security of such massively distributed systems is a major issue and advanced methods to improve their ability to detect and counter cybernetic attacks are needed. Evolutionary algorithms can automatically evolve and optimize sophisticated intrusion detection models, suitable for different applications. In this work, a hybrid evolutionary-fuzzy classification and regression algorithm is used to evolve detectors for several types of intrusions in a wireless sensor network. The ability of genetic programming and differential evolution to construct and optimize intrusion detectors for wireless sensor networks is evaluated on a recent intrusion detection data set capturing malicious activity in a wireless sensor network. (C) Springer Nature Switzerland AG 2021.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/TN01000024" target="_blank" >TN01000024: Národní centrum kompetence - Kybernetika a umělá inteligence</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Advances in Intelligent Systems and Computing. Volume 1263

ISBN

978-3-030-57795-7

ISSN

2194-5357

e-ISSN

—

Počet stran výsledku

12

Strana od-do

149-160

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Victoria

Datum konání akce

31. 8. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—