Static Malware Detection and Classification Using Machine Learning: A Random Forest Approach

Malware remains one of the most critical threats in the digital ecosystem, targeting both mobile and desktop platforms.Traditional signature-based detection techniques face limitations in identifying polymorphic and zero-day variants.This study proposes a static analysis-based approach using machine learning classifiers, focusing on Random Forest, Decision Tree, and Support Vector Machine (SVM).The dataset was collected from Mal-wareBazaar, and static features such as PE headers, entropy, and API calls were extracted.Experimental results show that SVM achieved the highest accuracy at 53.2%, while Decision Tree obtained the best F1-score at 61.1%, indicating stronger recall capabilities.Random Forest provided balanced results across all metrics with a shorter training time of 0.23 s, highlighting its efficiency for practical use.These findings demonstrate that while no single classifier dominates across all metrics, Random Forest offers a trade-off between performance and efficiency, making it suitable for large-scale static malware detection systems.