The rise of machine learning for detection and classification of malware: Research developments, trends and challenges
Published in Journal of Network and Computer Applciations, 2020
In this paper, we present a systematic review of machine learning (M.L.) approaches for malware detection. We classify traditional approaches into static, dynamic and hybrid approaches. We provide a detailed description of the features in a traditional M.L. workflow. We introduce new research directions such as deep learning and multimodal approaches. We discuss the research issues and challenges faced by security researchers.
Recommended citation: Daniel Gibert, Carles Mateu, Jordi Planes. (2020). "The rise of machine learning for detection and classification of malware: Research developments, trends and challenges." Journal of Network and Computer Applications.
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