Recent Advances in Machine-Learning Driven Intrusion Detection
in Transportation: Survey

D 2021

Recent Advances in Machine-Learning Driven Intrusion Detection in Transportation: Survey

BANGUI, Hind a Barbora BÜHNOVÁ

Základní údaje

Originální název

Recent Advances in Machine-Learning Driven Intrusion Detection in Transportation: Survey

Autoři

BANGUI, Hind (504 Maroko, domácí) a Barbora BÜHNOVÁ (203 Česká republika, domácí)

Vydání

Warsaw, Poland, The 11th International Symposium on Frontiers in Ambient and Mobile Systems, od s. 877-886, 10 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/00216224:14330/21:00121270

Organizační jednotka

Fakulta informatiky

ISSN

DOI

https://doi.org/10.1016/j.procs.2021.04.014

UT WoS

000672800000117

EID Scopus

2-s2.0-85106748750

Klíčová slova anglicky

Machine learning; VANET; UAV; Intrusion Detection Systems;Thrust; Security

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 23. 5. 2022 14:28, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

Rapid developments in Intelligent Transportation Systems (ITSs) have emerged as a new research field for building sustainable smart cities. VANET (vehicular ad hoc network) is one of the emergent transportation technologies that has a great impact on ensuring mainly traffic management and road safety in urban areas by effciently using data sharing among vehicles. To further increase the security and safety of passengers and drivers, ITSs are continually striving to make the fusion of emergent network technologies to provide more reliable and effcient services. Relating VANET to UAV (unmanned aerial vehicle) is an example of this fusion, where UAVs act as an assistant to vehicles aiming to extend the network connectivity while effciently avoiding obstacles (e.g., Buildings) and providing high data delivery ratios. However, VANET and UAV are still critical security subjects that must be addressed. Advanced Machine Learning (e.g., Deep Learning) techniques have recently been used to protect VANET and UAV communications against various cyber attacks that deteriorate the integrity, confidentiality, and availability of vehicular data. Thus, in this paper, we focus on reviewing related work on machine learning techniques for intrusion detection systems in VANET- and UAV-aided networks. We also highlight the main open research challenges in literature and provide hints for improving security in ITSs.

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 a Barbora BÜHNOVÁ. Recent Advances in Machine-Learning Driven Intrusion Detection in Transportation: Survey. Online. In Shakshuki, E; Yasar, A. The 11th International Symposium on Frontiers in Ambient and Mobile Systems. Warsaw, Poland: Elsevier Science, 2021, s. 877-886. ISSN 1877-0509. Dostupné z: https://doi.org/10.1016/j.procs.2021.04.014.

@inproceedings{1753491,
   author = {Bangui, Hind and Bühnová, Barbora},
   address = {Warsaw, Poland},
   booktitle = {The 11th International Symposium on Frontiers in Ambient and Mobile Systems},
   doi = {https://doi.org/10.1016/j.procs.2021.04.014},
   editor = {Shakshuki, E; Yasar, A},
   keywords = {Machine learning; VANET; UAV; Intrusion Detection Systems;Thrust; Security},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Warsaw, Poland},
   pages = {877-886},
   publisher = {Elsevier Science},
   title = {Recent Advances in Machine-Learning Driven Intrusion Detection in Transportation: Survey},
   url = {https://www.sciencedirect.com/science/article/pii/S1877050921007894},
   year = {2021}
}

TY  - CONF
ID  - 1753491
AU  - Bangui, Hind - Bühnová, Barbora
PY  - 2021
TI  - Recent Advances in Machine-Learning Driven Intrusion Detection in Transportation: Survey
PB  - Elsevier Science
CY  - Warsaw, Poland
KW  - Machine learning
KW  - VANET
KW  - UAV
KW  - Intrusion Detection Systems;Thrust
KW  - Security
UR  - https://www.sciencedirect.com/science/article/pii/S1877050921007894
N2  - Rapid developments in Intelligent Transportation Systems (ITSs) have emerged as a new research field for building sustainable smart cities. VANET (vehicular ad hoc network) is one of the emergent transportation technologies that has a great impact on ensuring mainly traffic management and road safety in urban areas by effciently using data sharing among vehicles. To further increase the security and safety of passengers and drivers, ITSs are continually striving to make the fusion of emergent network technologies to provide more reliable and effcient services. Relating VANET to UAV (unmanned aerial vehicle) is an example of this fusion, where UAVs act as an assistant to vehicles aiming to extend the network connectivity while effciently avoiding obstacles (e.g., Buildings) and providing high data delivery ratios. However, VANET and UAV are still critical security subjects that must be addressed. Advanced Machine Learning (e.g., Deep Learning) techniques have recently been used to protect VANET and UAV communications against various cyber attacks that deteriorate the integrity, confidentiality, and availability of vehicular data. Thus, in this paper, we focus on reviewing related work on machine learning techniques for intrusion detection systems in VANET- and UAV-aided networks. We also highlight the main open research challenges in literature and provide hints for improving security in ITSs.
ER  -

BANGUI, Hind a Barbora BÜHNOVÁ. Recent Advances in Machine-Learning Driven Intrusion Detection in Transportation: Survey. Online. In Shakshuki, E; Yasar, A. \textit{The 11th International Symposium on Frontiers in Ambient and Mobile Systems}. Warsaw, Poland: Elsevier Science, 2021, s.~877-886. ISSN~1877-0509. Dostupné z: https://doi.org/10.1016/j.procs.2021.04.014.

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