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 and Barbora BÜHNOVÁ

Basic information

Original name

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

Authors

BANGUI, Hind (504 Morocco, belonging to the institution) and Barbora BÜHNOVÁ (203 Czech Republic, belonging to the institution)

Edition

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

Organization unit

Faculty of Informatics

ISSN

DOI

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

UT WoS

000672800000117

EID Scopus

2-s2.0-85106748750

Keywords in English

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

Abstract

In the original language

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.

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 and 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, p. 877-886. ISSN 1877-0509. Available from: 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 and 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, p.~877-886. ISSN~1877-0509. Available from: https://doi.org/10.1016/j.procs.2021.04.014.

Přehled o publikaci