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Article

An explainable ensemble-based intrusion detection system for software-defined vehicle ad-hoc networks

Shakil Ibne Ahsan, Phil Legg, S. M. Iftekharul Alam 2025 International Journal of Multidisciplinary Studies and Innovative Researchs

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Abstract

Intrusion Detection Systems (IDS) are widely employed to detect and mitigate external network security events. Vehicle ad-hoc Networks (VANETs) continue to evolve, especially with developments related to Connected Autonomous Vehicles (CAVs). In this study, we explore the detection of cyber threats in vehicle networks through ensemble-based machine learning, to strengthen the performance of the learnt model compared to relying on a single model. We propose a model that uses Random Forest and CatBoost as our main ’investigators’, with Logistic Regression used to then reason on their outputs to make a final decision. To further aid analysis, we use SHAP (SHapley Additive exPlanations) analysis to examine feature importance towards the final decision stage. We use the Vehicular Reference Misbehavior (VeReMi) dataset for our experimentation and observe that our approach improves classification accuracy, and results in fewer misclassifications compared to previous works. Overall, this layered approach to decision-making - combining teamwork among models with an explainable view of why they act as they do - can help to achieve a more reliable and easy-to-understand cyber security solution for smart transportation networks.

Identifier Metadata

Identifier	110.0384/INT.2026.00358
Canonical	mdoi:110.0384/INT.2026.00358
Resolver URL	https://mdoi.org/110.0384/INT.2026.00358
Resource URL	Open resource
Document URL	Open document
Content Type	Article
Authors	Shakil Ibne Ahsan, Phil Legg, S. M. Iftekharul Alam
Year	2025
Depositor	International Journal of Multidisciplinary Studies and Innovative Researchs Organisation
Prefix	110.0384
Registered	June 24, 2026
Updated	June 24, 2026
Status	Active
Visibility	Public

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Registered by International Journal of Multidisciplinary Studies and Innovative Researchs

An explainable ensemble-based intrusion detection system for software-defined vehicle ad-hoc networks

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