Recent Advances in Machine-Learning Driven Intrusion Detection in Transportation: Survey
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F21%3A00121270" target="_blank" >RIV/00216224:14330/21:00121270 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.procs.2021.04.014" target="_blank" >http://dx.doi.org/10.1016/j.procs.2021.04.014</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.procs.2021.04.014" target="_blank" >10.1016/j.procs.2021.04.014</a>
Alternative languages
Result language
angličtina
Original language name
Recent Advances in Machine-Learning Driven Intrusion Detection in Transportation: Survey
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
<a href="/en/project/EF16_019%2F0000822" target="_blank" >EF16_019/0000822: CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
The 11th International Symposium on Frontiers in Ambient and Mobile Systems
ISBN
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ISSN
1877-0509
e-ISSN
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Number of pages
10
Pages from-to
877-886
Publisher name
Elsevier Science
Place of publication
Warsaw, Poland
Event location
Warsaw, Poland
Event date
Jan 1, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000672800000117