Improvement of Malware Classification Using Hybrid Feature Engineering
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
SN Computer Science
Abstract
Polymorphic 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.
Description
Keywords
Malware classification, Polymorphic malware, Machine learning, Feature engineering
Citation
Masabo, 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/s42979-019-0017-9