Evaluation of Machine Learning Algorithms for Intrusion Detection in SCADA Systems - Santos, Maria Eduarda Cher Benetis dos; Salmen, Fadir; Lucas, Thiago José; Ferreira, Tiago Martins; Cruz, Fernanda Mara; 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 AbertasDO - 10.5753/latinoware.2025.15968, 2025)
SCADA systems are widely used in critical industries such as energy and water for process monitoring and control. Due to their interconnection with communication networks, these systems are susceptible to various cyberattacks. This work seeks to detect attacks that could compromise the integrity and availability of SCADA systems, evaluating the performance of machine learning algorithms. Several tests were performed on the WUSTL-IIOT-2018 dataset in order to produce a comprehensive analysis of the performance of different classifiers. The results obtained demonstrated that the selected algorithms performed satisfactorily in the experiments carried out, highlighting their potential to strengthen the security of these critical systems