| Title |
A comparative study of Machine learning algorithms for VANET networks |
| ID_Doc |
43254 |
| Authors |
Ftaimi, S; Mazri, T |
| Title |
A comparative study of Machine learning algorithms for VANET networks |
| Year |
2020 |
| Published |
|
| DOI |
10.1145/3386723.3387829 |
| Abstract |
Vehicular Ad Hoc Networks (VANET) had incredible potential in improving road security, diminishing mishap rates and making the travel experience valuable for passengers. Like any other network, VANET too has problems and vulnerabilities that threaten its inherent nodes, and by implication, its reliability. In order to solve the security problems involving the VANET network, a lot of studies in machine learning algorithms have been undergone to improve the reliability of VANET by means of detecting intrusions and making prediction, ultimately, they have achieved satisfactory results. Therefore, the establishment of powerful VANET networks is significantly dependent on their security and protection alternatives, which is the subject of the present paper. This article starts with an overview of VANET Networks, then it proceeds to highlighting a variety of attacks that can threaten them. Next, we introduce the major concepts of machine learning before we conclude with the most frequently adopted artificial intelligence algorithms in the VANET networks |
| Author Keywords |
VANET; Machine learning; smart city; 5G; Attacks |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000649337600010 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Information Systems; Telecommunications |
| Research Area |
Computer Science; Telecommunications |
| PDF |
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