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 a Barbora BÜHNOVÁ

Základní údaje

Originální název

A Hybrid Data-driven Model for Intrusion Detection in VANET

Autoři

BANGUI, Hind (504 Maroko, domácí), Mouzhi GE (156 Čína, domácí) a Barbora BÜHNOVÁ (203 Česká republika, domácí)

Vydání

Warsaw, Poland, The 12th International Conference on Ambient Systems, Networks and Technologies (ANT 2021), od s. 516-523, 8 s. 2021

Nakladatel

Elsevier Science

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Polsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Kód RIV

RIV/00216224:14330/21:00121268

Organizační jednotka

Fakulta informatiky

ISSN

DOI

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

UT WoS

000672800000064

Klíčová slova anglicky

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

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 23. 5. 2022 14:27, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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.

Návaznosti

CZ.02.1.01/0.0/0.0/16_019/0000822, interní kód MU
(Kód CEP: EF16_019/0000822)

Název: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur (Akronym: C4e)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur, PO 1 Posilování kapacit pro kvalitní výzkum

EF16_019/0000822, projekt VaV

Název: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur

Citovat

BANGUI, Hind, Mouzhi GE a 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, s. 516-523. ISSN 1877-0509. Dostupné z: 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  - JOUR
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 a 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, s.~516-523. ISSN~1877-0509. Dostupné z: https://dx.doi.org/10.1016/j.procs.2021.03.065.

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