Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network

Yongqi Dong*, Kejia Chen, Yinxuan Peng, Zhiyuan Ma

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
5 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationProceedings of the 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)
PublisherIEEE
Pages2914-2919
Number of pages6
ISBN (Electronic)978-1-6654-6880-0
ISBN (Print)978-1-6654-6881-7
DOIs
Publication statusPublished - 2022
Event2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) - Macau, China
Duration: 8 Oct 202212 Oct 2022
Conference number: 25th

Conference

Conference2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)
Country/TerritoryChina
CityMacau
Period8/10/2212/10/22

Bibliographical note

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Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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