A Hybrid Data-driven Model for Intrusion Detection in VANET

D 2021

A Hybrid Data-driven Model for Intrusion Detection in VANET

BANGUI, Hind; Mouzhi GE and Barbora BÜHNOVÁ

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

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

References:

URL

RIV identification code

RIV/00216224:14330/21:00121268

Organization unit

Faculty of Informatics

ISSN

DOI

http://dx.doi.org/10.1016/j.procs.2021.03.065

UT WoS

000672800000064

EID Scopus

2-s2.0-85106674102

Keywords in English

VANET; Clustering; IDS; Coreset; Security ; Data Approximation

Abstract

In the original language

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

Cite

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.

@inproceedings{1753485,
   author = {Bangui, Hind and Ge, Mouzhi and Bühnová, Barbora},
   address = {Warsaw, Poland},
   booktitle = {The 12th International Conference on Ambient Systems, Networks and Technologies (ANT 2021)},
   doi = {http://dx.doi.org/10.1016/j.procs.2021.03.065},
   editor = {Shakshuki, E; Yasar, A},
   keywords = {VANET; Clustering; IDS; Coreset; Security ; Data Approximation},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Warsaw, Poland},
   pages = {516-523},
   publisher = {Elsevier Science},
   title = {A Hybrid Data-driven Model for Intrusion Detection in VANET},
   url = {https://www.sciencedirect.com/science/article/pii/S1877050921006967},
   year = {2021}
}

TY  - CONF
ID  - 1753485
AU  - Bangui, Hind - Ge, Mouzhi - Bühnová, Barbora
PY  - 2021
TI  - A Hybrid Data-driven Model for Intrusion Detection in VANET
PB  - Elsevier Science
CY  - Warsaw, Poland
KW  - VANET
KW  - Clustering
KW  - IDS
KW  - Coreset
KW  - Security
KW  - Data Approximation
UR  - https://www.sciencedirect.com/science/article/pii/S1877050921006967
N2  - 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.
ER  -

BANGUI, Hind; Mouzhi GE and Barbora BÜHNOVÁ. A Hybrid Data-driven Model for Intrusion Detection in VANET. Online. In Shakshuki, E; Yasar, A. \textit{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.

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