Malware Detection in Windows Operating Systems Using AI and In-Memory Process Analysis - Patricio, Jessica C. C.; Paiva, Carlos H.; Rodrigues, Renan L.; Lima, Vanessa C.; Gomes, Rafael L. (Anais do Latin-American Symposium on Dependable Computing (LADC); 2025: Anais do XIV Latin-American Symposium on Dependable Computing, 2025)
Currently, there is a noticeable increase in concern regarding the cybersecurity of corporate and personal data due to the rise of malicious software (Malware). In this context, this work presents a solution for intelligent malware detection in Windows Operating Systems by applying Artificial Intelligence (AI) techniques to memory process data. The solution aims to detect malware with minimal impact on the user’s device and network infrastructure, achieving scalability and appropriate response times through data compression and segmentation of functionalities by environment. The results demonstrate the feasibility of the solution, with the compression approach achieving approximately 60% reduction in data communication, while detection efficiency reaches around 99% within a few milliseconds.