A Hybrid Machine Learning Model for Intrusion Detection in VANET
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F22%3A00124963" target="_blank" >RIV/00216224:14330/22:00124963 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/s00607-021-01001-0" target="_blank" >https://doi.org/10.1007/s00607-021-01001-0</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00607-021-01001-0" target="_blank" >10.1007/s00607-021-01001-0</a>
Alternative languages
Result language
angličtina
Original language name
A Hybrid Machine Learning Model for Intrusion Detection in VANET
Original language description
While Vehicular Ad-hoc Network (VANET) is developed to enable effective vehicle communication and traffic information exchange, VANET is also vulnerable to different security attacks, such as DOS attacks. The usage of an intrusion detection system (IDS) is one possible solution for preventing attacks in VANET. However, dealing with a large amount of vehicular data that keep growing in the urban environment is still a critical challenge for IDSs. This paper, therefore, proposes a new machine learning model to improve the performance of IDSs by using Random Forest and a posterior detection based on coresets to improve the detection accuracy and increase detection efficiency. The experimental results show that the proposed machine learning model can significantly enhance the detection accuracy compared to classical application of machine learning models.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
<a href="/en/project/EF16_019%2F0000822" target="_blank" >EF16_019/0000822: CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Computing
ISSN
0010-485X
e-ISSN
1436-5057
Volume of the periodical
104
Issue of the periodical within the volume
3
Country of publishing house
AT - AUSTRIA
Number of pages
29
Pages from-to
503-531
UT code for WoS article
000687514800003
EID of the result in the Scopus database
2-s2.0-85113812633