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Machine Learning-driven Optimization for SVM-based Intrusion Detection System in Vehicular Ad Hoc Networks

Alsarhan, Ayoub; Alauthman, Mohammad; Alshdaifat, Esra’a; Al-Ghuwairi, Abdel-Rahman; Al-Dubai, Ahmed

Authors

Ayoub Alsarhan

Mohammad Alauthman

Esra’a Alshdaifat

Abdel-Rahman Al-Ghuwairi



Abstract

Machine Learning (ML) driven solutions have been widely used to secure wireless communications Vehicular ad hoc networks (VANETs) in recent studies. Unlike existing works, this paper applies support vector machine (SVM) for intrusion detection in VANET. The structure of SVM has many computation advantages, such as special direction at a finite sample and irrelevance between the complexity of algorithm and the sample dimension. Intrusion detection in VANET
is nonconvex and combinatorial problem. Thus, three intelligence optimization algorithms are used for optimizing the accuracy value of SVM classifier. These optimization algorithms include Genetic algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). Our results demonstrate that GA outperformed other optimization algorithms.

Citation

Alsarhan, A., Alauthman, M., Alshdaifat, E., Al-Ghuwairi, A.-R., & Al-Dubai, A. (2023). Machine Learning-driven Optimization for SVM-based Intrusion Detection System in Vehicular Ad Hoc Networks. Journal of Ambient Intelligence and Humanized Computing, 14(5), 6113-6122. https://doi.org/10.1007/s12652-021-02963-x

Journal Article Type Article
Acceptance Date Feb 8, 2021
Online Publication Date Feb 24, 2021
Publication Date 2023-05
Deposit Date Feb 12, 2021
Publicly Available Date Feb 25, 2022
Print ISSN 1868-5137
Electronic ISSN 1868-5145
Publisher BMC
Peer Reviewed Peer Reviewed
Volume 14
Issue 5
Pages 6113-6122
DOI https://doi.org/10.1007/s12652-021-02963-x
Keywords Intrusion detection, Smart city, support vector machine, security, misbehavior detection
Public URL http://researchrepository.napier.ac.uk/Output/2743240

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Machine Learning-driven Optimization For SVM-based Intrusion Detection System In Vehicular Ad Hoc Networks (accepted version) (712 Kb)
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