Design and analysis of efficient neural intrusion detection for wireless sensor networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10249611" target="_blank" >RIV/61989100:27240/21:10249611 - isvavai.cz</a>

Result on the web

<a href="https://onlinelibrary.wiley.com/doi/10.1002/cpe.6152" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1002/cpe.6152</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/cpe.6152" target="_blank" >10.1002/cpe.6152</a>

Result language

angličtina

Original language name

Design and analysis of efficient neural intrusion detection for wireless sensor networks

Original language description

Wireless sensor networks (WSNs) are important building blocks of the communication infrastructure in smart cities, intelligent transportation systems, Industry, Energy, and Agriculture 4.0, the Internet of Things, and other areas quickly adopting the concepts of fog and edge computing. Their cybernetic security is a major issue and efficient methods to improve their safety and reliability are required. Intrusion detection systems (IDSs) are complex systems that discover cybernetic attacks, detect malicious network traffic, and, in general, protect computer systems. Artificial neural networks are used by a variety of advanced intrusion detection systems with outstanding results. Their successful use in the specific conditions of WSNs requires efficient learning, adaptation, and inference. In this work, the acceleration of a neural intrusion detection model, developed specifically for wireless sensor networks, is proposed and studied, especially from the learning and classification accuracy and energy consumption points of view.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10200 - Computer and information sciences

Project

<a href="/en/project/TN01000024" target="_blank" >TN01000024: National Competence Center - Cybernetics and Artificial Intelligence</a><br>

Continuities

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

Publication year

2021

Confidentiality

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

Name of the periodical

Concurrency Computation Practice and Experience

ISSN

1532-0626

e-ISSN

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Volume of the periodical

33

Issue of the periodical within the volume

23

Country of publishing house

US - UNITED STATES

Number of pages

12

Pages from-to

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UT code for WoS article

000599461600001

EID of the result in the Scopus database

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