Comparative Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection in Electric Vehicle Systems - Noda Junior, Carlos Noboyuki; Lucas, Thiago José; Cruz, Fernanda Mara; Moraes, Eduardo Alves; Lopes, Alessandra de Souza; Costa, Kelton Augusto Pontara da (Anais do Congresso Latino-Americano de Software Livre e Tecnologias Abertas (Latinoware); 2025: Anais do XXII Congresso Latino-Americano de Software Livre e Tecnologias Abertas, 2025)
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.