Ember Feature Dataset Analysis For Malware Detection
Marian Şandor, Radu Marian Portase, Adrian Coleşa · 2023
Employing Machine Learning (ML) methodologies to address the issue of malware detection is an extensively researched subject. Researchers place particular emphasis on selecting an appropriate model and optimizing its performance through effective hyperparameter configuration. Ember is one of the primary datasets used for training and testing ML models for malware detection. In this study, we are interested in whether selecting a specific subset of the features defined in the Ember dataset would result in better-performing models. We seek to identify those feature groups that carry the most helpful information for the model to leverage and show that using only a combination of them can yield better performance than using the entire set of features.