Please refer to Table 4 of the following paper (for the first three datasets from the top): https://www.sciencedirect.com/science/article/pii/S0167404824000786
Korea University (KU): HCRL Attack & Defense Challenge dataset (with Hyundai Avante CN7) 2021 (link)
Paper: Hyunjae Kang, Byung Il Kwak, Young Hun Lee, Haneol Lee, Hwejae Lee and Huy Kang Kim. "Car Hacking and Defense Competition on In-Vehicle Network." Third International Workshop on Automotive and Autonomous Vehicle Security, 2021.
Real ORNL Automotive Dynamometer (ROAD) dataset (Unknown mid-2010s) 2020 (link)
Paper: Verma, M. E., Bridges, R. A., Iannacone, M. D., Hollifield, S. C., Moriano, P., Hespeler, S. C., ... & Combs, F. L. (2024). A comprehensive guide to CAN IDS data and introduction of the ROAD dataset. PLoS one, 19(1), e0296879.
DTU can-train-and-test dataset (with Chevrolet Impala, Chevrolet Traverse, Chevrolet Silverado, and Subaru Forester) 2023 (link)
Paper: Lampe, B., & Meng, W. (2024). can-train-and-test: A curated can dataset for automotive intrusion detection. Computers & Security, 140, 103777.
CAN-MIRGU dataset (Unknown manufactured in 2016) 2024 (link)
Paper: S. Rajapaksha, G. Madzudzo, H. Kalutarage, A. Petrovski and M.O. Al-Kadri. (2024). "CAN-MIRGU: A Comprehensive CAN Bus Attack Dataset from Moving Vehicles for Intrusion Detection System Evaluation." Symposium on Vehicles Security and Privacy (VehicleSec) 2024.
Korea University (KU): HCRL X-CANIDS dataset (with Hyundai LF Sonata) 2024 (link)
Paper: Jeong, S., Lee, S., Lee, H., & Kim, H. K. (2023). X-CANIDS: Signal-aware explainable intrusion detection system for controller area network-based in-vehicle network. IEEE Transactions on Vehicular Technology, 73(3), 3230-3246.
ETAS GmbH: SynCAN dataset (Synthetic) 2020 (link)
Paper: M. Hanselmann, T. Strauss, K. Dormann and H. Ulmer, "CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data," in IEEE Access, vol. 8, pp. 58194-58205, 2020.
Korea University (KU): HCRL Car-Hacking dataset (with Hyundai YF Sonata) 2018 (link)
Paper: Eunbi Seo, Hyun Min Song, and Huy Kang Kim. "GIDS: GAN based Intrusion Detection System for In-Vehicle Network." 2018 16th Annual Conference on Privacy, Security and Trust (PST). IEEE, 2018.
AV-HARM in AuSSE framework
AuSSE (Autonomous Vehicle Security and Safety Evaluation): A novel framework designed to assess both the cybersecurity and safety aspects of autonomous vehicles. It aims to answer the core question: How can cyberattacks impact operational safety in AVs?
VHARM (Vehicle Hierarchical Attack Representation Model): The graphical security model developed within the AuSSE framework. VHARM is used to identify attack paths, assess security risks, and visualize vulnerabilities in in-vehicle systems. It enables evaluation of attack scenarios, defense strategies, and security metrics, and is supported by a visualization tool using JSON-based architecture inputs for modeling
Related papers:
Nguyen NH, Cho JH, Moore TJ, Yoon S, Lim H, Nelson F, Bai G, Kim DD. AuSSE: A Novel Framework for Security and Safety Evaluation for Autonomous Vehicles. In2024 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks-Supplemental Volume (DSN-S) 2024 Jun 24 (pp. 1-5). IEEE.
Jungebloud T, Nguyen NH, Kim DD, Zimmermann A. Model-based structural and behavioural cybersecurity risk assessment in system designs. Computers & Security. 2025 Jun 11:104543.
Nguyen NH, Ge M, Cho JH, Moore TJ, Yoon S, Lim H, Nelson F, Bai G, Kim DD. Graphical security modelling for Autonomous Vehicles: A novel approach to threat analysis and defence evaluation. Computers & Security. 2025 Mar 1;150:104229.
AV-HARM
Source code is available at: https://github.com/ziz0301/AVHARM
Visualisation tool is available at: https://ziz0301.github.io/AVHARM/index.html