ArticleSensors (Basel, Switzerland)2023
Intrusion Detection in Vehicle Controller Area Network (CAN) Bus Using Machine Learning: A Comparative Performance Study.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Optimal attention deep learning based in-vehicle intrusion detection and classification model on CAN messages.Scientific reports · 2025Article
- Vision-Language Model-Based Local Interpretable Model-Agnostic Explanations Analysis for Explainable In-Vehicle Controller Area Network Intrusion Detection.Sensors (Basel, Switzerland) · 2025Article
- Securing the CAN bus using deep learning for intrusion detection in vehicles.Scientific reports · 2025Article
- BEPCD: an ensemble learning-based intrusion detection framework for in-vehicle CAN bus.PeerJ. Computer science · 2025Article
- Automotive Cybersecurity: A Survey on Frameworks, Standards, and Testing and Monitoring Technologies.Sensors (Basel, Switzerland) · 2024Review
- Sybil Attacks Detection and Traceability Mechanism Based on Beacon Packets in Connected Automobile Vehicles.Sensors (Basel, Switzerland) · 2024Article
- A Novel Hierarchical Security Solution for Controller-Area-Network-Based 3D Printing in a Post-Quantum World.Sensors (Basel, Switzerland) · 2023Article
- Design and Experimental Assessment of Real-Time Anomaly Detection Techniques for Automotive Cybersecurity.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electronic Control Units (ECUs) have been increasingly used in modern vehicles to control the operations of the vehicle, improve driving comfort, and safety. For the operation of the vehicle, these ECUs communicate using a Controller Area Network (CAN) protocol that has many security vulnerabilities. According to the report of Upstream 2022, more than 900 automotive cybersecurity incidents were reported in 2021 only. In addition to developing a more secure CAN protocol, intrusion detection can provide a path to mitigate cyberattacks on the vehicle. This paper proposes a machine learning-based intrusion detection system (IDS) using a Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN) and investigates the effectiveness of the IDS using multiple real-world datasets. The novelty of our developed IDS is that it has been trained and tested on multiple vehicular datasets (Kia Soul and a Chevrolet Spark) to detect and classify intrusion. Our IDS has achieved accuracy up to 99.9% with a high true positive and a low false negative rate. Finally, the comparison of our performance evaluation outcomes demonstrates that the proposed IDS outperforms the existing works in terms of its liability and efficiency to detect cyber-attacks with a minimal error rate.
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Registered trials
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