Comparative Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection in Electric Vehicle Systems
Resumo
With the growth of the electric car (EC) industry and market, it is natural that the use of cutting-edge technologies to provide inter-connectivity between cars and other devices will be implemented in their structures, both in the cars themselves and in their essential products, such as chargers. Given that such technologies open up opportunities for attacks on the cars’ critical infrastructure, the damage resulting from an eventual attack proves to be a major challenge for the cybersecurity area. Motivated by this scenario, this article proposes the development of an analysis of machine learning models for intrusion detection systems (IDS), to protect electric cars against intrusions. Most models achieved high accuracy, precision, recall, and F1-scores, with RNN LSTM leading in performance (0.999) despite higher computational cost. At the same time, Random Forest, AdaBoost, and CN2 offered strong results with lower training time, Naive Bayes proved efficient for limited resources, and Logistic Regression showed the weakest performance.
Palavras-chave:
Electric Vehicle, Machine Learning, Intrusion Detection System, Cybersecurity, CAN Bus
Referências
International Energy Agency, “Global ev outlook 2025,” International Energy Agency, Paris, Tech. Rep., 2025. [Online]. Available: [link]
D. Ronanki, A. Kelkar, and S. S. Williamson, “Extreme fast charging technology—prospects to enhance sustainable electric transportation,” Energies, vol. 12, no. 19, p. 3721, 2019.
J. Caltrider, M. Rykov, and Z. MacDonald, “It’s official: Cars are the worst product category we have ever reviewed for privacy,” Mozilla Foundation, 2023.
M. ElKashlan, M. S. Elsayed, A. D. Jurcut, and M. Azer, “A machine learning-based intrusion detection system for iot electric vehicle charging stations (evcss),” Electronics, vol. 12, no. 4, p. 1044, 2023.
S. Soltan, P. Mittal, and H. V. Poor, “BlackIoT:IoT botnet of high wattage devices can disrupt the power grid,” 27th USENIX Security Symposium (USENIX Security 18), pp. 15–32, 2018.
Z. Muhammad, Z. Anwar, B. Saleem, and J. Shahid, “Emerging cybersecurity and privacy threats to electric vehicles and their impact on human and environmental sustainability,” Energies, vol. 16, no. 3, p. 1113, 2023.
A. Drewek-Ossowicka, M. Pietrołaj, and J. Rumínski, “A survey of neural networks usage for intrusion detection systems,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 1, pp. 497–514, 2021.
M. D. Hossain, H. Inoue, H. Ochiai, D. Fall, and Y. Kadobayashi, “Lstmbased intrusion detection system for in-vehicle can bus communications,” IEEE Access, vol. 8, pp. 185 489–185 504, 2020.
Z. Garofalaki, D. Kosmanos, S. Moschoyiannis, D. Kallergis, and C. Douligeris, “Electric vehicle charging: A survey on the security issues and challenges of the open charge point protocol (ocpp),” IEEE Communications Surveys & Tutorials, vol. 24, no. 3, pp. 1504–1533, 2022.
S. Acharya, Y. Dvorkin, H. Pandžić, and R. Karri, “Cybersecurity of smart electric vehicle charging: A power grid perspective,” IEEE Access, vol. 8, pp. 214 434–214 453, 2020.
S. Acharya, Y. Dvorkin, and R. Karri, “Public plug-in electric vehicles + grid data: Is a new cyberattack vector viable?” IEEE Transactions on Smart Grid, vol. 11, no. 6, pp. 5099–5113, 2020.
X. Larriva-Novo, C. Sánchez-Zas, V. A. Villagrá, M. Vega-Barbas, and D. Rivera, “An approach for the application of a dynamic multi-class classifier for network intrusion detection systems,” Electronics, vol. 9, no. 11, p. 1759, 2020.
M. Basnet and M. H. Ali, “Deep learning-based intrusion detection system for electric vehicle charging station,” 2020 2nd International Conference on Smart Power & Internet Energy Systems (SPIES), pp. 408–413, 2020.
S. Hamdare, O. Kaiwartya, M. Aljaidi, M. Jugran, Y. Cao, S. Kumar, M. Mahmud, D. Brown, and J. Lloret, “Cybersecurity risk analysis of electric vehicles charging stations,” Sensors, vol. 23, no. 15, p. 6716, 2023.
T. Moulahi, S. Zidi, A. Alabdulatif, and M. Atiquzzaman, “Comparative performance evaluation of intrusion detection based on machine learning in in-vehicle controller area network bus,” IEEE Access, vol. 9, pp. 99 595–99 605, 2021.
H.-C. Lin, P. Wang, K.-M. Chao, W.-H. Lin, and J.-H. Chen, “Using deep learning networks to identify cyber attacks on intrusion detection for in-vehicle networks,” Electronics, vol. 11, no. 14, p. 2180, 2022.
M. A. S. Sardar, H. Saha, M. N. Sultan, and M. F. Rabbi, “Intrusion detection in electric vehicles using machine learning with model explainability,” J. Inf. Hiding Multim. Signal Process., vol. 14, no. 3, pp. 81–89, 2023.
M. M. Rahman, M. M. H. Chayan, K. Mehrin, A. Sultana, and M. M. Hamed, “Explainable deep learning for cyber attack detection in electric vehicle charging stations,” in Proceedings of the 11th International Conference on Networking, Systems, and Security, 2024, pp. 1–7.
F. Makhmudov, D. Kilichev, U. Giyosov, and F. Akhmedov, “Online machine learning for intrusion detection in electric vehicle charging systems,” Mathematics, vol. 13, no. 5, p. 712, 2025.
D. Ronanki, A. Kelkar, and S. S. Williamson, “Extreme fast charging technology—prospects to enhance sustainable electric transportation,” Energies, vol. 12, no. 19, p. 3721, 2019.
J. Caltrider, M. Rykov, and Z. MacDonald, “It’s official: Cars are the worst product category we have ever reviewed for privacy,” Mozilla Foundation, 2023.
M. ElKashlan, M. S. Elsayed, A. D. Jurcut, and M. Azer, “A machine learning-based intrusion detection system for iot electric vehicle charging stations (evcss),” Electronics, vol. 12, no. 4, p. 1044, 2023.
S. Soltan, P. Mittal, and H. V. Poor, “BlackIoT:IoT botnet of high wattage devices can disrupt the power grid,” 27th USENIX Security Symposium (USENIX Security 18), pp. 15–32, 2018.
Z. Muhammad, Z. Anwar, B. Saleem, and J. Shahid, “Emerging cybersecurity and privacy threats to electric vehicles and their impact on human and environmental sustainability,” Energies, vol. 16, no. 3, p. 1113, 2023.
A. Drewek-Ossowicka, M. Pietrołaj, and J. Rumínski, “A survey of neural networks usage for intrusion detection systems,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 1, pp. 497–514, 2021.
M. D. Hossain, H. Inoue, H. Ochiai, D. Fall, and Y. Kadobayashi, “Lstmbased intrusion detection system for in-vehicle can bus communications,” IEEE Access, vol. 8, pp. 185 489–185 504, 2020.
Z. Garofalaki, D. Kosmanos, S. Moschoyiannis, D. Kallergis, and C. Douligeris, “Electric vehicle charging: A survey on the security issues and challenges of the open charge point protocol (ocpp),” IEEE Communications Surveys & Tutorials, vol. 24, no. 3, pp. 1504–1533, 2022.
S. Acharya, Y. Dvorkin, H. Pandžić, and R. Karri, “Cybersecurity of smart electric vehicle charging: A power grid perspective,” IEEE Access, vol. 8, pp. 214 434–214 453, 2020.
S. Acharya, Y. Dvorkin, and R. Karri, “Public plug-in electric vehicles + grid data: Is a new cyberattack vector viable?” IEEE Transactions on Smart Grid, vol. 11, no. 6, pp. 5099–5113, 2020.
X. Larriva-Novo, C. Sánchez-Zas, V. A. Villagrá, M. Vega-Barbas, and D. Rivera, “An approach for the application of a dynamic multi-class classifier for network intrusion detection systems,” Electronics, vol. 9, no. 11, p. 1759, 2020.
M. Basnet and M. H. Ali, “Deep learning-based intrusion detection system for electric vehicle charging station,” 2020 2nd International Conference on Smart Power & Internet Energy Systems (SPIES), pp. 408–413, 2020.
S. Hamdare, O. Kaiwartya, M. Aljaidi, M. Jugran, Y. Cao, S. Kumar, M. Mahmud, D. Brown, and J. Lloret, “Cybersecurity risk analysis of electric vehicles charging stations,” Sensors, vol. 23, no. 15, p. 6716, 2023.
T. Moulahi, S. Zidi, A. Alabdulatif, and M. Atiquzzaman, “Comparative performance evaluation of intrusion detection based on machine learning in in-vehicle controller area network bus,” IEEE Access, vol. 9, pp. 99 595–99 605, 2021.
H.-C. Lin, P. Wang, K.-M. Chao, W.-H. Lin, and J.-H. Chen, “Using deep learning networks to identify cyber attacks on intrusion detection for in-vehicle networks,” Electronics, vol. 11, no. 14, p. 2180, 2022.
M. A. S. Sardar, H. Saha, M. N. Sultan, and M. F. Rabbi, “Intrusion detection in electric vehicles using machine learning with model explainability,” J. Inf. Hiding Multim. Signal Process., vol. 14, no. 3, pp. 81–89, 2023.
M. M. Rahman, M. M. H. Chayan, K. Mehrin, A. Sultana, and M. M. Hamed, “Explainable deep learning for cyber attack detection in electric vehicle charging stations,” in Proceedings of the 11th International Conference on Networking, Systems, and Security, 2024, pp. 1–7.
F. Makhmudov, D. Kilichev, U. Giyosov, and F. Akhmedov, “Online machine learning for intrusion detection in electric vehicle charging systems,” Mathematics, vol. 13, no. 5, p. 712, 2025.
Publicado
22/10/2025
Como Citar
NODA JUNIOR, Carlos Noboyuki; LUCAS, Thiago José; CRUZ, Fernanda Mara; MORAES, Eduardo Alves; LOPES, Alessandra de Souza; COSTA, Kelton Augusto Pontara da.
Comparative Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection in Electric Vehicle Systems. In: CONGRESSO LATINO-AMERICANO DE SOFTWARE LIVRE E TECNOLOGIAS ABERTAS (LATINOWARE), 22. , 2025, Foz do Iguaçu/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
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p. 236-242.
DOI: https://doi.org/10.5753/latinoware.2025.16318.
