Impact of Data Anonymization in Machine Learning Models - Pimenta, Ivo; Silva, Douglas; Moura, Evellin; Silveira, Matheus; Gomes, Rafael Lopes (Anais Estendidos do Latin-American Symposium on Dependable and Secure Computing (LADC), 2024)
Data leakage compromises companies’ confidentiality and directly impacts existing privacy laws, where it is necessary to ensure integration with legacy systems to avoid harming the performance of their services while efficiently using the data for training machine learning (ML) models. Within this context, this work applies a technique to anonymize the data and train ML models in the context of DDoS attacks, aiming to evaluate the performance of this anonymization technique.