AUTOMOTIVE ELECTRONICS AND CYBERSECURITY: A SYSTEMATIC REVIEW OF SECURITY ATTACKS AND COUNTERMEASURES
DOI:
https://doi.org/10.28925/2663-4023.2025.28.760Keywords:
automotive electronics, automotive cybersecurity, vehicle networks, data encryption, message authentication, intrusion detection technology, trusted platform module, Crypto-Engine conceptAbstract
Modern automotive electronics are a complex system of sensors, electronic control units (ECUs) and actuators connected through various types of automotive networks to control and monitor the condition of the vehicle. In addition, modern vehicles are increasingly connected to the outside world through vehicle-to-everything (V2X) technologies. These create new attack surfaces that increase the cybersecurity risk for modern vehicles. With the advent of intelligent transportation structures, the focus has shifted to the structure of coordinated inter-vehicle systems, symbolized by the integration of infrastructure, people, vehicles, urban areas and the environment. This combination of computer technology and automotive innovation has raised numerous questions about cyberattacks on cars, which play a significant role in the development and use of automotive technology. Advanced wireless technology allows vehicles to exchange and transmit information with each other and around them in real time, which will help reduce accidents, congestion and improve the efficiency of mobile vehicles. Many advanced technologies, such as cloud computing, artificial intelligence, V2X technology, and advanced driver assistance systems, are increasingly being used in cars, making vehicles more intelligent to provide convenient services to people and ensure the safety of drivers and passengers. However, as cars become more connected to the Internet, wireless networks, each other, and other transportation network infrastructure, the risk of cyberattacks is becoming more and more problematic. This review article first analyzes the vulnerabilities of vehicle networks and identifies the main cybersecurity attacks on vehicles. Technologies for improving the cybersecurity of vehicle networks are analyzed in the following technological areas: data encryption, message authentication, network intrusion detection, building a trusted platform, and implementing the Crypto-Engine concept.
Downloads
References
Adu-Kyere, A., Nigussie, E., & Isoaho, J. (2023). Self-Aware CybersecurityArchitecture for AutonomousVehicles: Security throughSystem-Level Accountability. Sensors, 23, 8817. https://doi.org/10.3390/s23218817
Aldhyani, T.H.H., & Alkahtani, H. (2022). Attacks to Automatous Vehicles: A Deep Learning Algorithm for Cybersecurity. Sensors, 22, 360.
Al-Jarrah, O.Y., El Haloui, K., Dianati, M., & Maple, C. (2023). A Novel Detection Approach of Unknown Cyber-Attacks for Intra-Vehicle Networks Using Recurrence Plots and Neural Networks. IEEE Open J. Veh. Technol., 4, 271–280.
Andrade, R, Dias Santos, M. M., Justo J. F., Yoshioka, L. R., Hof, H.-J., & Kleinschmidt J. H. (2023). Security architecture for automotive communication networks with CAN FD. Computers & Security, 129, 103203
Askaripoor, H., Hashemi Farzaneh, M., & Knoll, A. (2022). E/E Architecture Synthesis: Challenges and Technologies. Electronics, 11, 518. https://doi.org/10.3390/electronics11040518
Baldanzi, L., Crocetti, L., Bertolucci, M., Fanucci, L., & Saponara, S. (2019). Analysis of Cybersecurity Weakness in Automotive In-Vehicle Networking and Hardware Accelerators for Real-time Cryptography. In book: Applications in Electronics Pervading Industry, Environment and Society, 11–18. https://doi.org/10.1007/978-3-030-11973-7_2
Bari, B. S., Yelamarthi, K., & Ghafoor, S. (2023). Intrusion Detection in Vehicle Controller Area Network (CAN) Bus Using Machine Learning: A Comparative Performance Study. Sensors, 23, 3610.
Casillo, M., Coppola, S., Santo, M.D., Pascale, F., & Santonicola, E. (2019). Embedded intrusion detection system for detecting attacks over CAN-BUS. In Proceedings of the 4th International Conference on System Reliability and Safety.
Chandwani, A., Dey, S., & Mallik, A. (2020). Cybersecurity of Onboard Charging Systems for Electric Vehicles-Review, Challenges and Countermeasures. IEEE Access, 8, 226982–226998.
De Vincenzi, M., Costantino, G., Matteucci, I., Fenzl, F., Plappert, C., Rieke, R., & Zelle, D. (2024). A Systematic Review on Security Attacks and Countermeasures in Automotive Ethernet. ACM Comput. Surv., 56, 135.
El-Rewini, Z., Sadatsharan, K., Selvaraj, D. F., Plathottam, S. J., & Ranganathan, P. (2020). Cybersecurity Challenges in Vehicular Communications. Vehicular Communications, 23, 100214.
El-Rewini, Z., Sadatsharan, K., Sugunaraj, N., Selvaraj, D.F., Plathottam, S.J., & Ranganathan, P. (2020). Cybersecurity Attacks in Vehicular Sensors. IEEE Sens. J., 20, 13752–13767.
Fernandes, R. S. (2023) Vehicle Electronics and Cybersecurity: Current Interactions, Vulnerabilities, and Recommendations. European mobility group. Report from UTAC. https://www.mobilitygroup.eu/emg-members-other-news/vehicle-electronics-and-cybersecurity
Guan, T., Han, Y., Kang, N., Tang, N., Chen, X., & Wang, S. (2022). An Overview of Vehicular Cybersecurity for Intelligent Connected Vehicles. Sustainability, 14, 5211. https://doi.org/10.3390/su14095211
Harvey, J., & Kumar, S. (2020). A survey of intelligent transportation systems security: Challenges and solutions. Proc. IEEE 6th Int. Conf. Big Data Security Cloud (BigDataSecurity) IEEE Int. Conf. High Perform. Smart Comput. (HPSC) IEEE Int. Conf. Intell. Data Security (IDS), 263-268. https://ieeexplore.ieee.org/document/9123012
Islam, R., & Refat, R. U. D. (2020). Improving CAN bus security by assigning dynamic arbitration Ids. Journal of Transportation Security, 13, 19-31. https://link.springer.com/article/10.1007/s12198-020-00208-0
Khalid Khan, S., Shiwakoti, N., & Stasinopoulos, P. (2022). A Conceptual System Dynamics Model for Cybersecurity Assessment of Connected and Autonomous Vehicles. Accident Analysis & Prevention, 165, 106515.
Khan, Z., Chowdhury, M., Islam, M., Huang, C.Y., & Rahman, M. (2019). In-vehicle false information attack detection and mitigation framework using machine learning and software defined networking. arXiv:1906.10203.
Kifor, C. V., & Popescu A. (2024). Automotive Cybersecurity: A Survey on Frameworks, Standards, and Testing and Monitoring Technologies. Sensors, 24, 6139. https://doi.org/10.3390/s24186139
Klimushin, P., Solianyk, T., Kolisnyk, T., & Mozhaev, O. (2021). Potential application of hardware protected symmetric authentication microcircuitsto ensure the securityof internet of things. Advanced Information Systems, 5, 103-111. https://doi.org/10.20998/2522-9052.2021.3.14
Klimushyn, P., Solianyk, T., Mozhaev, O., Nosov, V., Kolisnyk, T., & Yanov, V. (2021), Hardware support procedures for asymmetric authentication of the internet of things. Innovative Technologies and Scientific Solutions for Industries, 4 (18), 31–39. https://doi.org/10.30837/ITSSI.2021.18.031
Klimushyn, P., Solianyk, T., Mozhaiev, O., Gnusov, Y., Manzhai, O., & Svitlychny, V. (2022). Crypto-resistantmethods and random number generatorsin internet of things (IOT) devices. Innovative Technologies and Scientific Solutions for Industries, 2(20), 22–34. https://doi.org/10.30837/ITSSI.2022.20.022
Kong, Q.L., Lu, R.X., Yin, F., & Cui, S.G. (2021). Blockchain-Based Privacy-Preserving Driver Monitoring for MaaS in the Vehicular IoT. IEEE Trans. Veh. Technol., 70, 3788–3799.
Kukkala, V. K., Thiruloga, S. V., & Pasricha, S. (2022). Roadmap for Cybersecurity in Autonomous Vehicles. IEEE Consumer Electronics Magazine, 11, 13–23. https://doi.org/10.48550/arXiv.2201.10349
Lampe, B., & Meng, W. (2023). Intrusion Detection in the Automotive Domain: A Comprehensive Review. IEEE Communications Surveys & Tutorials, 25(4). https://doi.org/10.1109/COMST.2023.3309864
Ma, R., Cao, J., Feng, D., Li, H., Li, X., & Xu, Y. (2022). A robust authentication scheme for remote diagnosis and maintenance in 5G V2N. J. Netw. Comput. Appl, 198, 103281.
Mokhadder, M., Zachos, M., & Potter, J. (2023). Evaluation of vehicle system performance of an SAE J1939-91C network security implementation, Proc. WCX SAE World Congr. Exp., 6. https://saemobilus.sae.org/content/2023-01-0041
Pape, S., Syed-Winkler, S., Garcia, A.M., Chah, B., Bkakria, A., Hiller, M., Walcher, T., Lombard, A., Abbas-Turki, A., & Yaich, R. (2023). A Systematic Approach for Automotive Privacy Management. In Proceedings of the 7th ACM Computer Science in Cars Symposium CSCS.
Pervez, L., Khan, A., & Ain, N. (2020). Cyber Security Challenge in an Automobile. International Journal of Scientific and Research Publications, 10(12), 162–166. https://doi.org/10.29322/IJSRP.10.12.2020.p10815
Qayyum, A., Usama, M., Qadir, J., & Al-Fuqaha, A. (2020). Securing connected & autonomous vehicles: Challenges posed by adversarial machine learning and the way forward. IEEE Commun. Surv. Tutor., 22, 998–1026.
Rajapaksha, S., Kalutarage, H., Al-Kadri, M.O., Petrovski, A., Madzudzo, G., & Cheah, M. (2023). AI-Based Intrusion Detection Systems for In-Vehicle Networks: A Survey. ACM Comput. Surv., 55, 237.
Rodriguez, E., Otero, B., & Canal, R. (2023). A Survey of Machine and Deep Learning Methods for Privacy Protection in the Internet of Things. Sensors, 23, 1252.
Sabaliauskaite, G., Bryans, J., Jadidbonab, H., Ahmad, F., Shaikh, S., & Wooderson, P. (2024). TOMSAC-Methodology for Trade-off Management between Automotive Safety and Cyber Security. Comput. Secur., 140, 103798.
Shin, Y., & Jeon, S. (2024). MQTree: Secure OTA Protocol Using MQTT and MerkleTree. Sensors, 24, 1447.
Tiberti, W., Civino, R., Gavioli, N., Pugliese, M., & Santucci, F. (2023). A Hybrid-Cryptography Engine for Securing Intra-Vehicle Communications. Appl. Sci. 13, 13024. https://doi.org/10.3390/app132413024
VanWyk, F., Wang, Y., Khojandi, A., & Masoud, N. (2020). Real-time sensor anomaly detection and identification in automated vehicles. IEEE Trans. Intell. Transp. Syst., 21, 1264–1276.
Vehicle Electronics and Cybersecurity: Current Interactions, Vulnerabilities, and Recommendations. European mobility group. Report from UTAC. (2023). https://www.mobilitygroup.eu/emg-members-other-news/vehicle-electronics-and-cybersecurity
Wang, P., Wu, X., & He, X. (2020). Modeling and Analyzing Cyberattack Effects on Connected Automated Vehicular Platoons. Transportation Research Part C: Emerging Technologies, 115, 102625.
Wang, Z., & Li, X. (2021). Intrusion prevention system design. Proc. Int. Conf. Inf. Eng. Appl. (IEA), 218, 375-382, URL: https://link.springer.com/chapter/10.1007/978-1-4471-4847-0_47
Wu, Z., Tian, E., & Chen, H. (2023). Covert Attack Detection for LFC Systems of Electric Vehicles: A Dual Time-Varying Coding Method. IEEE/ASME Trans. Mechatron, 3(28), 681–691.
Wu,W., Li, R., Xie, G., An, J., Bai, Y., Zhou, J., & Li, K. (2020). A survey of intrusion detection for in-vehicle networks. IEEE Trans. Intell. Transp. Syst., 21, 919–933.
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Петро Клімушин, Віталій Світличний, Юрій Гнусов, Юрій Онищенко
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
