FETA: A systematic and efficient approach for feature engineering on anti-static and anti-dynamic malware analysis

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.