Rabadi, D., Loo, J. Y., Narayanan, A., Wang, Y., Teo, S. G., & Truong-Huu, T. (2025). FETA: A systematic and efficient approach for feature engineering on anti-static and anti-dynamic malware analysis. Journal of Information Security and Applications, 93, 104104. https://doi.org/10.1016/j.jisa.2025.104104
Abstract:
Malware detection is a critical but very challenging task in cybersecurity. The eternal competition between malware authors
(cyber attackers) and security analysts (detectors) is a never-ending game in which malware evolves rapidly and becomes more
sophisticated as cyber attackers constantly evolve their tactics to evade detection. Such competition raises the demand for new
automated malware detection techniques to keep pace with malware evolution and address sophisticated malware. This paper
presents an empirical study that analyzes the effectiveness of static and dynamic features using machine learning algorithms.
We propose FETA, a systematic approach for Feature Engineering on anti-sTatic and anti-dynAmic malware analysis. FETA
combines static and dynamic features through feature aggregation and model integration techniques to improve detection accuracy
and robustness. Extensive experiments on a real-world dataset show that the aggregation of static and dynamic features outperforms
individual feature sets, achieving a detection rate of 98.06%. Additionally, we provide insights into feature selection and conduct
a deep analysis of misclassified samples. This research contributes to the development of more effective and efficient malware detection techniques for enhanced cybersecurity
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done