Modelagem das Áreas de Risco de Sistemas de Detecção de Intrusão para Cálculo de Métricas de Privacidade

• Jessica Yumi Nakano Sato USP
• Daniel Macêdo Batista USP

DOI: https://doi.org/10.5753/sbseg_estendido.2023.233937

Abstract

Although recent regulations require software developers to become severely concerned about privacy, recommendations related to this have been around for years. Despite the relatively old understanding that computational systems need to guarantee the user’s privacy, it has been difficult to find works that evaluate the regulations in existing systems, mainly because privacy metrics vary with the application domain. This paper presents preliminary results from the modeling of risk areas aiming to measure privacy metrics of an IDS based on machine learning. It is shown that it was possible to adapt the principles of the literature to our domain.

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