A Hybrid Data-driven Model for Intrusion Detection in VANET

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F21%3A00121268" target="_blank" >RIV/00216224:14330/21:00121268 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.procs.2021.03.065" target="_blank" >http://dx.doi.org/10.1016/j.procs.2021.03.065</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.procs.2021.03.065" target="_blank" >10.1016/j.procs.2021.03.065</a>

Result language
angličtina
Original language name
A Hybrid Data-driven Model for Intrusion Detection in VANET
Original language description
Nowadays, VANET (Vehicular Ad-hoc NETwork) has gained increasing attention from many researchers with its various applications, such as enhancing traffic safety by collecting and disseminating traffic event information. This increased interest in VANET has necessitated greater scrutiny of machine learning (ML) methods used for improving the security capabilities of intrusion detection systems (IDSs), such as the need to solve computationally intensive ML problems due to the increased vehicular data. Therefore, in this paper, we propose a hybrid ML model to enhance the performance of IDSs by dealing with the explosive growth in computing power and the need for detecting malicious incidents timely. The proposed approach mainly uses the advantages of Random Forest to detect known network intrusions. Besides, there is a post-detection phase to detect possible novel intruders by using the advantages of coresets and clustering algorithms. Our approach is evaluated over a very recent IDS dataset named CICIDS2017. The preliminary results show that the proposed hybrid model can increase the utility of IDSs.
Czech name
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Czech description
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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)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
The 12th International Conference on Ambient Systems, Networks and Technologies (ANT 2021)
ISBN
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ISSN
1877-0509
e-ISSN
—
Number of pages
8
Pages from-to
516-523
Publisher name
Elsevier Science
Place of publication
Warsaw, Poland
Event location
Warsaw, Poland
Event date
Jan 1, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000672800000064

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