Abstract
With the application of information technology in automotive electronic system and the development of internet of vehicles, automobiles face increasing security threats. Controller area network (CAN) is the main bus system for communication between electronic control units (ECUs) in modern automobiles. CAN bus network easily suffers from cyberattacks because it lacks security protection mechanisms. Intrusion detection is an effective method to defend a network against attacks. However, the detection accuracy may be affected by the change in driving environment or driving behavior and the occurrence of unknown attacks. A two-stage intrusion detection method based on Deep Neural Network (DNN) and Incremental Learning (IL) is proposed to improve detection performance. In an offline training stage, DNN is applied to obtain basic classification model using marked actual CAN data. Then, in the online detection and updating stage, the model is updated with IL technology based on new unlabeled data, at the same time performing intrusion detection. Experimental results show that the proposed method has high detection accuracy and good generalization ability.
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
References
Wu, F.: A survey of intrusion detection for in-vehicle networks. IEEE Trans. Intell. Transp. Syst. 21(3), 919–933 (2020)
Lokman, S.-F.: Intrusion detection system for automotive Controller Area Network (CAN) bus system: a review. EURASIP J. Wirel. Commun. Netw. 2019, 184 (2019)
Lee, H., Jeong, S.H., Kim, S.: 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), IEEE, Piscataway (2017)
Cho, K.-T., Shin, K.: Viden: Attacker identification on in-vehicle networks. In: 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1109–1123. ACM, New York (2017)
Subir Halder, F., Mauro Conti, S.: COIDS: a clock offset based intrusion detection system for controller area networks. In: 21st International Conference on Distributed Computing and Networking (ICDCN 2020), pp. 1–10. ACM, New York (2020)
Ning, J., Liu, J.: An experimental study towards attacker identification in automotive networks. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp. 1–6. IEEE, Piscataway (2019)
Kai, T., Zhongwei, L., Wenqi, J., Yadong, G., Weiming, T.: In-vehicle CAN bus anomaly detection algorithm based on linear chain condition random field. In: 2019 IEEE 19th International Conference on Communication Technology (ICCT), pp. 1153–1159. IEEE, Piscataway (2020)
Avatefipour, O., et al.: An intelligent secured framework for cyberattack detection in electric vehicles’ can bus using machine learning. IEEE Access 7, 127580–127592 (2019)
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)
Yuanda Yang, F.: Intrusion detection for in-vehicle network by using single GAN in connected vehicles. J. Circ. Syst. Comput. 30(1), 2150007 (2021)
Kang, M.-J., Kang, J.-W.: Intrusion detection system using deep neural network for in-vehicle network security. PLoS ONE 11(6), e0155781 (2016)
Song, H.M., Woo, J., Kim, H.K.: In-vehicle network intrusion detection using deep convolutional neural network. Veh. Commun. 21, 100198 (2020)
Leslie, N.: An unsupervised learning approach for in-vehicle network intrusion detection. In: 2021 55th Annual Conference on Information Sciences and Systems (CISS), pp. 1–4. IEEE, Piscataway (2021)
Mehedi, S.K., Anwar, A., Rahman, Z., AhmedMehedi, K.: Deep transfer learning based intrusion detection system for electric vehicular networks. Sensors 21(14), 4736 (2021)
Qiang, H., Luo, F.: Review of secure communication approaches for in-vehicle network. Int. J. Autom. Technol. 19, 879–894 (2018)
Young, C., Zambreno, J., Olufowobi, H., Bloom, G.: Survey of automotive controller area network intrusion detection systems. IEEE Design Test 36(6), 48–55 (2019)
German. I.F.: Continual lifelong learning with neural networks: a review. Neural Netw. 113, 54–71 (2019)
Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. Proc. Natl. Acad. Sci. USA 114(13), 3521–3526 (2017)
CAR-HACKING DATASET Homepage. https://ocslab.hksecurity.net/Datasets/CAN-intrusion-dataset
Acknowledgment
This work is supported by the Natural Science Foundation of Hunan Province of China (No. 2020JJ4058) and Guangxi Key Laboratory of Crytography and Information Security (No. GCIS201920) and is supported in part by the National Natural Science Foundation of China (No. 62072175).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lin, J., Wei, Y., Li, W., Long, J. (2022). Intrusion Detection System Based on Deep Neural Network and Incremental Learning for In-Vehicle CAN Networks. In: Wang, G., Choo, KK.R., Ko, R.K.L., Xu, Y., Crispo, B. (eds) Ubiquitous Security. UbiSec 2021. Communications in Computer and Information Science, vol 1557. Springer, Singapore. https://doi.org/10.1007/978-981-19-0468-4_19
Download citation
DOI: https://doi.org/10.1007/978-981-19-0468-4_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-0467-7
Online ISBN: 978-981-19-0468-4
eBook Packages: Computer ScienceComputer Science (R0)