A Hybrid Data-driven Model for Intrusion Detection in VANET

BANGUI, Hind, Mouzhi GE and Barbora BÜHNOVÁ. A Hybrid Data-driven Model for Intrusion Detection in VANET. Online. In Shakshuki, E; Yasar, A. The 12th International Conference on Ambient Systems, Networks and Technologies (ANT 2021). Warsaw, Poland: Elsevier Science, 2021, p. 516-523. ISSN 1877-0509. Available from: https://dx.doi.org/10.1016/j.procs.2021.03.065.

Basic information

• Original name: A Hybrid Data-driven Model for Intrusion Detection in VANET
• Authors: BANGUI, Hind (504 Morocco, belonging to the institution), Mouzhi GE (156 China, belonging to the institution) and Barbora BÜHNOVÁ (203 Czech Republic, belonging to the institution).
• Edition: Warsaw, Poland, The 12th International Conference on Ambient Systems, Networks and Technologies (ANT 2021), p. 516-523, 8 pp. 2021.
• Publisher: Elsevier Science

Other information

• Original language: English
• Type of outcome: Proceedings paper
• Field of Study: 10200 1.2 Computer and information sciences
• Country of publisher: Poland
• Confidentiality degree: is not subject to a state or trade secret
• Publication form: electronic version available online
• WWW: URL
• RIV identification code: RIV/00216224:14330/21:00121268
• Organization unit: Faculty of Informatics
• ISSN: 1877-0509
• Doi: http://dx.doi.org/10.1016/j.procs.2021.03.065
• UT WoS: 000672800000064
• Keywords in English: VANET; Clustering; IDS; Coreset; Security ; Data Approximation
• Tags: International impact, Reviewed

Abstract

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.

Links

• CZ.02.1.01/0.0/0.0/16_019/0000822, interní kód MU(CEP code: EF16_019/0000822): Name: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur (Acronym: C4e)

Investor: Ministry of Education, Youth and Sports of the CR, CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence, Priority axis 1: Strengthening capacities for high-quality research

• EF16_019/0000822, research and development project: Name: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur