Abstract
With the development of intelligent networked vehicles, the research on the safety of in-vehicle networks has gradually become a hot spot. CAN (controller area network) is the most widely used in-vehicle network bus, and its safety problem has become the most critical problem to be solved in the development process of intelligent networked vehicles. This paper aims at the in-vehicle can be used in intelligent networked vehicles Bus network, its communication characteristics and security problems are analyzed and dissected. Meanwhile, with the increase in demand for in-vehicle network communication applications, the corresponding attacks have also increased year by year. Therefore, this paper only increases the anomaly detection of in-vehicle application log in the anomaly detection of CAN bus, aiming to detect the abnormal behavior of vehicles in an all-around way. To solve the field data lacking's problem, we collect a data set containing several types of data from multiple channels, including different types of attack can bus messages. Due to the different elements in the message having different effects on the classification results, the attention mechanism is introduced to give different weights to different messages and log data segments, which increases the effect of classification detection.
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References
Gao, Y., Iqbal, S., et al.: Performance and power analysis of high-density multi-GPGPU architectures: a preliminary case study. In: IEEE 17th HPCC (2015)
Zhao, H., Chen, M., et al.: A novel pre-cache schema for high performance android system. Futur. Gener. Comput. Syst. 56, 766–772 (2016)
Qiu, M., Xue, C., Shao, Z., Sha, E.: Energy minimization with soft real-time and DVS for uniprocessor and multiprocessor embedded systems. In: IEEE DATE Conference, pp. 1–6 (2007)
Qiu, L., Gai, K., Qiu, M.: Optimal big data sharing approach for tele-health in cloud computing. In: IEEE SmartCloud, pp. 184–189 (2016)
Qiu, H., Qiu, M., Memmi, G., Ming, Z., Liu, M.: A dynamic scalable blockchain based communication architecture for IoT. In: Qiu, M. (ed.) Smart Blockchain, vol. 11373, pp. 159–166. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05764-0_17
Qiu, H., Qiu, M., Lu, Z.: Selective encryption on ECG data in body sensor network based on supervised machine learning. Inf. Fusion 55, 59–67 (2020)
Checkoway, S., et al.: Comprehensive experimental analyses of automotive attack surfaces. In: USENIX Security Symposium (2011)
Miller, C., Valasek, C.: Remote exploitation of an unaltered passenger vehicle. Black Hat USA, vol. 2015 (2015)
Woo, S., Jo, H.J., Lee, D.H.: A practical wireless attack on the connected car and security protocol for in-vehicle can. IEEE Trans. Intell. Transp. Syst. 16(2), 993–1006 (2015)
Szilagyi, C., Koopman, P.: Low cost multicast authentication via validity voting in time-triggered embedded control networks. In: Proceedings of 5th Workshop on Embedded Systems Security, p. 10. ACM (2010)
Lin, C.-W., Sangiovanni-Vincentelli, A.: Cyber-security for the controller area network (CAN) communication protocol. In: Proceedings International Conference on Cyber Security. IEEE (2012)
Groza, B., Murvay, S.: Efficient protocols for secure broad cast in controller area networks. IEEE Trans. Ind. Inform. 9(4), 2034–2042 (2013)
Jaynes, M., Dantu, R., Varriale, R., Evans, N.: Automating ecu identifification for vehicle security. In: 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 632–635 (2016)
Moore, M.R., Bridges, R.A., Combs, F.L., Starr, M.S., Prowell, S.J.: Modeling inter-signal arrival times for accurate detection of can bus signal injection attacks: a data-driven approach to in-vehicle intrusion detection. In: ACM Proceedings of CISRC, p. 11 (2017)
Moore, M.R., Bridges, R.A., Combs, F.L., Ander son, A.L.: Data-driven extraction of vehicle states from CAN bus traffific for cyber protection and safety. Consumer Electronics Magazine (to appear). https://goo.gl/8LUvNH
Miller, C., Valasek, C.: Adventures in automotive networks and control units. Def Con 21, 260–264 (2013)
Cho, K.-T., Shin, K.G.: Error handling of in-vehicle networks makes them vulnerable. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 1044–1055. ACM (2016)
Zhang, Z., Wu, J., et al.: Jamming ACK attack to wireless networks and a mitigation approach. IEEE GLOBECOM Conference, pp. 1–5 (2008)
Thakur, K., Qiu, M., Gai, K., Ali, M.: An investigation on cyber security threats and security models. In: IEEE CSCloud (2015)
Gai, K., Qiu, M., Sun, X., Zhao, H.: Security and privacy issues: a survey on FinTech. In: Qiu, M. (ed.) Smart Computing and Communication, pp. 236–247. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52015-5_24
Gai, K., Qiu, M., Elnagdy, S.: A novel secure big data cyber incident analytics framework for cloud-based cybersecurity insurance. In: IEEE BigDataSecurity (2016)
Hoppe, T., Kiltz, S., Dittmann, J.: Security threats to automotive CAN networks – practical examples and selected short-term countermeasures. In: Harrison, M.D., Sujan, M.-A. (eds.) Computer Safety, Reliability, and Security, pp. 235–248. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87698-4_21
Hoppe, T., Kiltz, S., Dittmann, J.: Applying intrusion detection to automotive it-early in sights and remaining challenges. J. Inf. Assur. Secur. (JIAS) 4(6), 226–235 (2009)
Gmiden, M., Gmiden, M.H., Trabelsi, H.: An intrusion detection method for securing in-vehicle CAN bus. In: Proceedings of Sciences and Techniques of Automatic Control and Computer Engineering. IEEE (2016)
Song, H.M., Kim, H.R., Kim, H.K.: Intrusion detection system based on the analysis of time intervals of can messages for in-vehicle network. In: 2016 International Conference on Information Networking (ICOIN), pp. 63–68. IEEE (2016)
Cho, K.-T., Shin, K.G.: Fingerprinting electronic control units for vehicle intrusion detection. In: USENIX Security Symposium, pp. 911–927 (2016)
Taylor, A., Leblanc, S., Japkowicz, N.: Anomaly detection in automobile control network data with long short-term memory networks. In: Proceedings of Sciences and Techniques of Automatic Control and Computer Engineering. IEEE (2016)
Dupont, G., Hartog, J.D., Etalle, S., Lekidis, A.: Evaluation framework for network intrusion detection systems for in-vehicle CAN
Acknowledgements
This work was supported by the 2020 Industrial Internet Innovation and Development Project-the Key Project of Intelligent Connected Vehicle Safety Inspection Platform (Tender No. TC200H01S), and the Beijing Advanced Innovation Center for Big Data and Brain Computing, and supported by Project of Comprehensive Protection Platform for Industrial Enterprise Network Security.
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Tan, X., Zhang, C., Li, B., Ge, B., Liu, C. (2022). Anomaly Detection System of Controller Area Network (CAN) Bus Based on Time Series Prediction. In: Qiu, M., Gai, K., Qiu, H. (eds) Smart Computing and Communication. SmartCom 2021. Lecture Notes in Computer Science, vol 13202. Springer, Cham. https://doi.org/10.1007/978-3-030-97774-0_29
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DOI: https://doi.org/10.1007/978-3-030-97774-0_29
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