Hybrid Malware Analysis using Static and Dynamic Techniques with Machine Learning

The rapid growth of digital infrastructure has been accompanied by a dramatic increase in the complexity and volume of malicious software, making traditional signature-based detection techniques increasingly ineffective. Modern malware employs advanced evasion strategies such as code obfuscation, packing, encryption, and polymorphism, which allow it to bypass conventional security systems. While static analysis offers fast and safe inspection of executable files, it struggles to detect heavily obfuscated malware. Dynamic analysis provides valuable insight into real execution behavior but suffers from high computational overhead and is vulnerable to sandbox and environment-aware evasion techniques.To address these challenges, this paper presents a hybrid malware analysis framework that integrates static and dynamic analysis techniques with machine learning to achieve accurate, reliable, and resilient malware detection. The proposed system extracts complementary static features from the internal structure of executable files and dynamic features from their runtime behavior in a controlled environment. These features are fused to create a comprehensive representation of program characteristics, which is then used to train supervised machine learning models for effective malware classification.Experimental evaluation on real-world datasets demonstrates that the proposed hybrid approach significantly outperforms traditional single-analysis methods, achieving higher detection accuracy, lower false-positive rates, and improved robustness against obfuscation and sandbox evasion. The results confirm that combining multiple analysis techniques with intelligent learning mechanisms provides a practical and scalable solution for next-generation cybersecurity systems.