Abstract
Recently, autonomous and connected vehicles have gained popularity, revolutionizing consumer mobility. On the other hand, they are also becoming new targets exposing new attack vectors and vulnerabilities that may lead to critical consequences. In this paper, we propose VNGuard, an intrusion detection system for two critical in-vehicle networks (IVNs), namely, the Local Interconnect Network (LIN) and the Automotive Ethernet (AE). In the proposed system, LIN messages and AE network packets are converted into images, and then a state-of-the-art deep convolutional neural networks (DCNN) model is applied to not only detect anomalous traffic, but also to classify types of attacks. Our experimental results showed that the VNGuard achieves more than 96% detection accuracy for LIN and 99% attack classification accuracy for AE. In addition, the VNGuard is able to perform the intrusion detection within 3 ms for LIN and 4 ms for AE significantly within the latency constraint required by the autonomous and connected vehicles to achieve human-level safety.
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
Alkhatib, N., Ghauch, H., Danger, J.L.: SOME/IP intrusion detection using deep learning-based sequential models in automotive ethernet networks. In: 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 0954–0962. IEEE (2021)
Ashraf, J., Bakhshi, A.D., Moustafa, N., Khurshid, H., Javed, A., Beheshti, A.: Novel deep learning-enabled LSTM autoencoder architecture for discovering anomalous events from intelligent transportation systems. IEEE Trans. Intell. Transp. Syst. 22(7), 4507–4518 (2020)
Buscemi, A., Ponaka, M., Fotouhi, M., Koebel, C., Jomrich, F., Engel, T.: An intrusion detection system against rogue master attacks on gPTP. In: IEEE Vehicular Technology Conference (VTC2023-Spring), Florence, 20–23 June 2023 (2023)
Cheng, P., Xu, K., Li, S., Han, M.: TCAN-IDS: intrusion detection system for internet of vehicle using temporal convolutional attention network. Symmetry 14(2) (2022)
Deichmann, J., Ebel, E., Heineke, K., Heuss, R., Kellner, M., Steiner, F.: Autonomous driving’s future: convenient and connected. https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/autonomous-drivings-future-convenient-and-connected(Jan 2023)
Ernst, J.M., Michaels, A.J.: LIN bus security analysis. In: 44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018, pp. 2085–2090. IEEE (2018)
Han, M.L., Kwak, B.I., Kim, H.K.: TOW-IDS: intrusion detection system based on three overlapped wavelets for automotive ethernet. IEEE Trans. Inf. Forensics Secur. 18, 411–422 (2023)
Han, M.L., Kwak, B., Kim, H.K.: TOW-IDS: automotive ethernet intrusion dataset (2022). https://doi.org/10.21227/bz0w-zc12
Huang, T., Zhou, J., Bytes, A.: ATG: an attack traffic generation tool for security testing of in-vehicle CAN bus. In: Proceedings of the 13th International Conference on Availability, Reliability and Security, ARES 2018. Association for Computing Machinery, New York (2018)
Huang, T., Zhou, J., Wang, Y., Cheng, A.: On the security of in-vehicle hybrid network: status and challenges. In: Liu, J.K., Samarati, P. (eds.) ISPEC 2017. LNCS, vol. 10701, pp. 621–637. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-72359-4_38
Jeong, S., Jeon, B., Chung, B., Kim, H.K.: Convolutional neural network-based intrusion detection system for AVTP streams in automotive ethernet-based networks. Veh. Commun. 29, 100338 (2021)
Koyama, T., Tanaka, M., Miyajima, A., Ukai, S., Sugashima, T., Egawa, M.: SOME/IP intrusion detection system using real-time and retroactive anomaly detection. In: 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), pp. 1–7. IEEE (2022)
Limbasiya, T., Teng, K.Z., Chattopadhyay, S., Zhou, J.: A systematic survey of attack detection and prevention in connected and autonomous vehicles. Veh. Commun. 100515 (2022)
Lin, S.C., et al.: The architectural implications of autonomous driving: constraints and acceleration. In: Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 751–766 (2018)
Man, D., Zeng, F., Lv, J., Xuan, S., Yang, W., Guizani, M.: AI-based intrusion detection for intelligence internet of vehicles. IEEE Consum. Electron. Mag. 12(1), 109–116 (2023). https://doi.org/10.1109/MCE.2021.3137790
Páez, F., Kaschel, H.: A proposal for data authentication, data integrity and replay attack rejection for the LIN bus. In: 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), pp. 1–7. IEEE (2021)
Páez, F., Kaschel, H.: Towards a robust computer security layer for the LIN bus. In: 2021 IEEE International Conference on Automation/XXIV Congress of the Chilean Association of Automatic Control (ICA-ACCA), pp. 1–8. IEEE (2021)
Páez, F., Kaschel, H.: Design and testing of a computer security layer for the LIN bus. Sensors 22(18), 6901 (2022)
Qin, H., Yan, M., Ji, H.: Application of controller area network (CAN) bus anomaly detection based on time series prediction. Veh. Commun. 27, 100291 (2021)
Rajapaksha, S., Kalutarage, H., Al-Kadri, M.O., Petrovski, A., Madzudzo, G., Cheah, M.: AI-based intrusion detection systems for in-vehicle networks: a survey. ACM Comput. Surv. 55(11), 1–40 (2023)
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. IEEE (2018)
Song, H.M., Woo, J., Kim, H.K.: In-vehicle network intrusion detection using deep convolutional neural network. Veh. Commun. 21, 100198 (2020)
Takahashi, J., et al.: Automotive attacks and countermeasures on LIN-bus. J. Inf. Process. 25, 220–228 (2017)
Teck, A.Y.K., Garbelini, M.E., Chattopadhyay, S., Zhou, J.: VitroBench: manipulating in-vehicle networks and COTS ECUs on your bench: a comprehensive test platform for automotive cybersecurity research. Veh. Commun. 100649 (2023)
Upstream: 2023 global automotive cybersecurity report (2023). https://upstream.auto/reports/global-automotive-cybersecurity-report/
Zhang, Y., Liu, T., Zhao, H., Ma, C.: Risk analysis of CAN bus and ethernet communication security for intelligent connected vehicles. In: 2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID), pp. 291–295. IEEE (2021)
Acknowledgements
This research/project is supported by the National Research Foundation, Singapore, and Land Transport Authority under Urban Mobility Grand Challenge (UMGC-L011). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore and Land Transport Authority. The work is also supported by Singapore Ministry of Education (MOE) Tier 2 Award MOE-T2EP20122-0015. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of MOE.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Aung, Y.L., Wang, S., Cheng, W., Chattopadhyay, S., Zhou, J., Cheng, A. (2023). VNGuard: Intrusion Detection System for In-Vehicle Networks. In: Athanasopoulos, E., Mennink, B. (eds) Information Security. ISC 2023. Lecture Notes in Computer Science, vol 14411. Springer, Cham. https://doi.org/10.1007/978-3-031-49187-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-49187-0_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-49186-3
Online ISBN: 978-3-031-49187-0
eBook Packages: Computer ScienceComputer Science (R0)