Comparative study on supervised versus semi-supervised machine learning for anomaly detection of In-vehicle CAN network

Dong, Yongqi, Chen, Kejia, Peng, Yinxuan and Ma, Zhiyuan 2022. Comparative study on supervised versus semi-supervised machine learning for anomaly detection of In-vehicle CAN network. Presented at: 25th International Conference on Intelligent Transportation Systems (ITSC), Macau, China, 8-12 October 2022. Proceedings of 25th International Conference on Intelligent Transportation Systems. IEEE, pp. 2914-2919. 10.1109/ITSC55140.2022.9922235

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Abstract

As the central nerve of the intelligent vehicle control system, the in-vehicle network bus is crucial to the security of vehicle driving. One of the best standards for the in-vehicle network is the Controller Area Network (CAN bus) protocol. However, the CAN bus is designed to be vulnerable to various attacks due to its lack of security mechanisms. To enhance the security of in-vehicle networks and promote the research in this area, based upon a large scale of CAN network traffic data with the extracted valuable features, this study comprehensively compared fully-supervised machine learning with semi-supervised machine learning methods for CAN message anomaly detection. Both traditional machine learning models (including single classifier and ensemble models) and neural network based deep learning models are evaluated. Furthermore, this study proposed a deep autoencoder based semi-supervised learning method applied for CAN message anomaly detection and verified its superiority over other semi-supervised methods. Extensive experiments show that the fully-supervised methods generally outperform semi-supervised ones as they are using more information as inputs. Typically the developed XGBoost based model obtained state-of-the-art performance with the best accuracy (98.65%), precision (0.9853), and ROC AUC (0.9585) beating other methods reported in the literature.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
ISBN:	9781665468817
Last Modified:	06 May 2023 02:37
URI:	https://orca.cardiff.ac.uk/id/eprint/154532

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