Machine Learning-based Spyware Detection Systems: An Undersampling Performance Analysis - Souza, Diogo Santos de Oliveira M. de; Ramos, Maria Clara Souza; Oliveira Junior, Wilson Cabral de; Lucas, Thiago José; Ferreira, Tiago Martins; 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.2025.15967, 2025)
This study examined Machine Learning (ML) techniques with and without the application of Undersampling to improve Spyware identification in Intrusion Detection Systems (IDS). The problem of data imbalance and how Undersampling can help select more relevant and diverse data subsets was discussed. The research adopts a methodical approach, which includes a systematic literature review, selection of relevant data, appropriate preprocessing, and evaluation of the performance of ML classifiers. The findings suggest that the use of Undersampling techniques can significantly influence the effectiveness of IDS, with certain classifiers showing notable improvements after being trained with these methods.