Abstract
Nowadays, vehicles have become more complex due to the increased number of electronic control units communicating through in-vehicle networks. Controller area network (CAN) is one of the most used protocols for in-vehicle networks. Still, it lacks a conventional security infrastructure, making it highly vulnerable to numerous attacks. The Fuzzy attack is one of the most challenging attacks for in-vehicle networks because of its randomly spoofed injected messages similar to the legitimate traffic and its numerous physical effects on the vehicle. In this paper, we focus on Fuzzy attack detection in the internal vehicle network by investigating the performances of ensemble learning techniques to mitigate this attack. We evaluated their efficiency on realistic datasets and on a new advanced stealthy attack dataset with physical impacts on the vehicle. eXtreme, Light, and Category Gradient Boosting, as well as Bagging ensemble learning techniques, in particular, showed a considerable improvement in detection performance in terms of accuracy, training and testing time reduction, and a decreased false alarm rate.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Alshammari, A., Zohdy, M.A., Debnath, D., Corser, G.: Classification approach for intrusion detection in vehicle systems. Wireless Eng. Technol. 9(4), 79–94 (2018)
Barletta, V.S., Caivano, D., Nannavecchia, A., Scalera, M.: Intrusion detection for in-vehicle communication networks: an unsupervised kohonen som approach. Future Internet 12(7), 119 (2020)
Boyd, K., Eng, K.H., Page, C.D.: Area under the precision-recall curve: point estimates and confidence intervals. In: Blockeel, H., Kersting, K., Nijssen, S., Zelezny, F. (eds.) Machine Learning and Knowledge Discovery in Databases. LNCS, vol. 8190. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40994-3_29
Han, M.L., Kwak, B.I., Kim, H.K.: Anomaly intrusion detection method for vehicular networks based on survival analysis. Vehic. Commun. 14, 52–63 (2018)
Hossain, M.D., Inoue, H., Ochiai, H., Fall, D., Kadobayashi, Y.: Lstm-based intrusion detection system for in-vehicle can bus communications. IEEE Access 8, 185489–185502 (2020)
Kalkan, S.C., Sahingoz, O.K.: In-vehicle intrusion detection system on controller area network with machine learning models. In: 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, pp. 1–6 (2020)
Lee, H., Jeong, S.H., Kim, H.K.: 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), pp. 57–5709. IEEE (2017)
Lokman, S.F., Othman, A.T., Abu-Bakar, M.H.: Intrusion detection system for automotive controller area network (can) bus system: a review. EURASIP J. Wireless Commun. Netw. 2019(1), 1–17 (2019)
Oehlert, P.: Violating assumptions with fuzzing. IEEE Security Privacy 3(2), 58–62 (2005)
Seo, E., Song, H.M., Kim, H.K.: Gids: gan based intrusion detection system for in-vehicle network. In: 2018 16th Annual Conference on Privacy, Security and Trust (PST), pp. 1–6 (2018). https://doi.org/10.1109/PST.2018.8514157
Tian, D., et al.: An intrusion detection system based on machine learning for can-bus. In: International Conference on Industrial Networks and Intelligent Systems, pp. 285–294. Springer (2017) https://doi.org/10.1007/978-3-642-40994-3_29
Verma, M.E., Iannacone, M.D., Bridges, R.A., Hollifield, S.C., Kay, B., Combs, F.L.: Road: the real ornl automotive dynamometer controller area network intrusion detection dataset (with a comprehensive can ids dataset survey and guide). arXiv preprint arXiv:2012.14600 (2020)
Vuong, T.P., Loukas, G., Gan, D.: Performance evaluation of cyber-physical intrusion detection on a robotic vehicle. In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing. IEEE, pp. 2106–2113 (2015)
Vuong, T.P., Loukas, G., Gan, D., Bezemskij, A.: Decision tree-based detection of denial of service and command injection attacks on robotic vehicles. In: 2015 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6. IEEE (2015)
Wu, W., et al.: A survey of intrusion detection for in-vehicle networks. IEEE Trans. Intell. Transp. Syst. 21(3), 919–933 (2019)
Yang, L., Moubayed, A., Hamieh, I., Shami, A.: Tree-based intelligent intrusion detection system in internet of vehicles. In: 2019 IEEE global communications conference (GLOBECOM), pp. 1–6. IEEE (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Swessi, D., Idoudi, H. (2022). Comparative Study of Ensemble Learning Techniques for Fuzzy Attack Detection in In-Vehicle Networks. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 450. Springer, Cham. https://doi.org/10.1007/978-3-030-99587-4_51
Download citation
DOI: https://doi.org/10.1007/978-3-030-99587-4_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-99586-7
Online ISBN: 978-3-030-99587-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)