Ensemble based feature selection approach for malware analysis

Ensemble based feature selection approach for malware analysis

연구 분야: Safety

학회: IC3-2022: Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing

—Internet users around the world use laptops, desktops, and mobile devices for at least some of their linked activities. The cybercriminals noticed this and boosted various types of cyberattacks. A malware attack is a common cyberattack in which malicious software performs nefarious activities on the victim’s machine. The focus of this paper is on malware detection. The usefulness of feature selection approaches for Windows executable file classification for malware detection was explored in this research. We studied various feature selection approaches in this study. In addition, we covered several supervised machine learning techniques that helps in malware detection. Finally, we employ an ensemble filter-based feature selection approach to increase the overall classifier’s accuracy and generalisation capabilities.