A hybrid machine learning model for intrusion detection in VANET

While Vehicular Ad-hoc Network (VANET) is developed to enable effective vehicle communication and traffic information exchange, VANET is also vulnerable to different security attacks, such as DOS attacks. The usage of an intrusion detection system (IDS) is one possible solution for preventing attack...

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Vydáno v:Computing Ročník 104; číslo 3; s. 503 - 531
Hlavní autoři: Bangui, Hind, Ge, Mouzhi, Buhnova, Barbora
Médium: Journal Article
Jazyk:angličtina
Vydáno: Vienna Springer Vienna 01.03.2022
Springer Nature B.V
Témata:
Accuracy
Artificial Intelligence
Big Data
Collaboration
Communication
Communications traffic
Computer Appl. in Administrative Data Processing
Computer Communication Networks
Computer Science
Data exchange
Efficiency
Information Systems Applications (incl.Internet)
Intrusion detection systems
Machine learning
Mobile ad hoc networks
Smart cities
Software Engineering
Special Issue Article
Traffic congestion
Urban environments
Vehicles
68T01
Intrusion
Machine learning
Classification
VANET
Security
Clustering
Coresets
Random Forest
ISSN:0010-485X, 1436-5057
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Popis
Shrnutí:While Vehicular Ad-hoc Network (VANET) is developed to enable effective vehicle communication and traffic information exchange, VANET is also vulnerable to different security attacks, such as DOS attacks. The usage of an intrusion detection system (IDS) is one possible solution for preventing attacks in VANET. However, dealing with a large amount of vehicular data that keep growing in the urban environment is still an critical challenge for IDSs. This paper, therefore, proposes a new machine learning model to improve the performance of IDSs by using Random Forest and a posterior detection based on coresets to improve the detection accuracy and increase detection efficiency. The experimental results show that the proposed machine learning model can significantly enhance the detection accuracy compared to classical application of machine learning models.
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ISSN:0010-485X
1436-5057
DOI:10.1007/s00607-021-01001-0