Yes, CARLA CAN: Extending the CARLA Simulator for In-Vehicle Network Cybersecurity Experimentation

• Luigi F. Marques da Luz UFPE https://orcid.org/0009-0000-8781-9219
• Vinicius L. Ventura UFPE https://orcid.org/0009-0003-8242-6004
• Divanilson R. Campelo UFPE https://orcid.org/0000-0001-8851-2665

DOI: https://doi.org/10.5753/sbrc_estendido.2026.23250

Resumo

Modern vehicles rely on in-vehicle networks (IVNs), but protocols such as CAN lack built-in security, exposing critical systems to cyber threats. Existing platforms often require real vehicles or hardware, limiting accessibility. This work presents a software-based platform for automotive cybersecurity research and experimentation. It integrates a virtual CAN bus with dynamic driving scenarios using the CARLA simulator. A modular design enables ECUs (Electronic Control Units), attackers, and intrusion detection systems to operate as independent nodes. The platform enables monitoring and manipulation of CAN traffic without hardware, providing a reproducible environment for research, teaching, dataset generation, and evaluation of IVN security.

Referências

Checkoway, S., McCoy, D., Kantor, B., Anderson, D., Shacham, H., Savage, S., Koscher, K., Czeskis, A., Roesner, F., and Kohno, T. (2011). Comprehensive experimental analyses of automotive attack surfaces. In 20th USENIX Security Symposium (USENIX Security 11).

Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., and Koltun, V. (2017). CARLA: An open urban driving simulator. In Proceedings of the 1st Annual Conference on Robot Learning, pages 1–16.

Evenchick, E. (2015). An introduction to the CANard toolkit. In Blackhat Conference, pages 1–8.

Falch, M. (2024). CAN DBC File Explained – A Simple Intro. [link]. Accessed: 2026-01-14.

Gay, C., Toyama, T., and Oguma, H. (2020). Resistant automotive miniature network. In Proceedings of the Chaos Computer Congress, Leipzig, Germany, pages 27–30.

Häckel, T., Meyer, P., Korf, F., and Schmidt, T. C. (2022). Secure time-sensitive software-defined networking in vehicles. IEEE Transactions on Vehicular Technology, 72(1):35–51.

Han, M. L., Kwak, B. I., and Kim, H. K. (2018). Anomaly intrusion detection method for vehicular networks based on survival analysis. Vehicular Communications, 14:52–63.

Intrepid Control Systems (2025). Vehicle spy. [link]. Accessed: 2025-12-08.

Koscher, K., Czeskis, A., Roesner, F., Patel, S., Kohno, T., Checkoway, S., McCoy, D., Kantor, B., Anderson, D., Shacham, H., and Savage, S. (2010). Experimental security analysis of a modern automobile. In 2010 IEEE Symposium on Security and Privacy, pages 447–462.

Lampe, B. and Meng, W. (2024). can-train-and-test: A curated CAN dataset for automotive intrusion detection. Computers & Security, 140:103777.

Lee, H., Jeong, S. H., and Kim, H. K. (2017). OTIDS: A novel intrusion detection system for in-vehicle network by using remote frame. In 2017 15th Annual Conference on Privacy, Security and Trust (PST), pages 57–5709.

Nie, S., Liu, L., and Du, Y. (2017). Free-fall: Hacking tesla from wireless to CAN bus. Briefing, Black Hat USA, 25(1):16.

Seo, E., Song, H. M., and Kim, H. K. (2018). GIDS: GAN based intrusion detection system for in-vehicle network. In 2018 16th Annual Conference on Privacy, Security and Trust (PST), pages 1–6.

Song, H. M., Woo, J., and Kim, H. K. (2020). In-vehicle network intrusion detection using deep convolutional neural network. Vehicular Communications, 21:100198.

Toyama, T., Yoshida, T., Oguma, H., and Matsumoto, T. (2018). PASTA: Portable automotive security testbed with adaptability. London, blackhat Europe.

VALUE-PASTA Contributors (2024). VALUE-PASTA: Low-Cost Automotive Security Testbed. [link]. Accessed: 2025-12-08.

Vector Informatik GmbH (2025). Vector automotive software tools. [link]. Accessed: 2025-12-08.

Wen, H., Chen, Q. A., and Lin, Z. (2020). Plug-N-Pwned: Comprehensive vulnerability analysis of OBD-II dongles as a new Over-the-Air attack surface in automotive IoT. In 29th USENIX security symposium (USENIX Security 20), pages 949–965.

Wu, W., Li, R., Xie, G., An, J., Bai, Y., Zhou, J., and Li, K. (2020). A survey of intrusion detection for in-vehicle networks. IEEE Transactions on Intelligent Transportation Systems, 21(3):919–933.

Yang, L., Moubayed, A., and Shami, A. (2021). MTH-IDS: A multitiered hybrid intrusion detection system for internet of vehicles. IEEE Internet of Things Journal, 9(1):616–632.