Masabo, EmmanuelKaawaase, Kyanda SwaibSansa‑Otim, JulianneNgubiri, JohnHanyurwimfura, Damien2022-09-052022-09-052020Masabo, E., Kaawaase, K. S., Sansa-Otim, J., Ngubiri, J., & Hanyurwimfura, D. (2020). Improvement of malware classification using hybrid feature engineering. SN Computer Science, 1(1), 1-14. https://doi.org/10.1007/s42979-019-0017-9https://doi.org/10.1007/s42979-019-0017-9https://nru.uncst.go.ug/handle/123456789/4555Polymorphic malware has evolved as a major threat in Computer Systems. Their creation technology is constantly evolving using sophisticated tactics to create multiple instances of the existing ones. Current solutions are not yet able to sufficiently address this problem. They are mostly signature based; however, a changing malware means a changing signature. They, therefore, easily evade detection. Classifying them into their respective families is also hard, thus making elimination harder. In this paper, we propose a new feature engineering (NFE) approach for a better classification of polymorphic malware based on a hybrid of structural and behavioural features. We use accuracy, recall, precision, and F score to evaluate our approach. We achieve an improvement of 12% on accuracy between raw features and NFE features. We also demonstrated the robustness of NFE on feature selection as compared to other feature selection techniques.enMalware classificationPolymorphic malwareMachine learningFeature engineeringConference Proceedings