Comparison Of Random Forest and Neural Network for Portable Executable Malware Classification: Perbandingan Random Forest dan Neural Network pada Klasifikasi Malware Portable Executable - Nugroho, Agung; Umam, Chaerul (Universitas Muhammadiyah Sidoarjo, 2026)
General Background: The rapid growth of information technology has increased the complexity of cyber threats, with malware attacks posing significant risks to computer systems, particularly those based on the Windows operating system. Specific Background: Portable Executable files contain structured statistical attributes that can be utilized to distinguish malware from benign software using machine learning techniques. Knowledge Gap: Despite extensive use of machine learning in malware detection, comparative evidence using identical Portable Executable statistical features and consistent evaluation settings remains limited. Aims: This study aims to compare the classification performance of Random Forest and Neural Network models in malware detection based on Portable Executable statistical features. Results: Using the ClaMP Integrated Dataset comprising 5,184 samples and 70 static features, Random Forest achieved an accuracy, precision, recall, and F1-score of 99.14%, while the Neural Network obtained consistent scores of 98.18% across all evaluation metrics. Novelty: This research presents a direct and controlled comparison of ensemble and neural-based classifiers using identical preprocessing pipelines, default model configurations, and balanced Portable Executable datasets. Implications: The findings demonstrate that ensemble-based approaches provide stable and reliable performance for Portable Executable malware classification and offer a practical foundation for automated machine learning–based cybersecurity systems. Highlights • Random Forest Achieved The Highest Classification Scores Across All Metrics• Portable Executable Statistical Features Provided Clear Malware Separation• Ensemble Learning Demonstrated Strong Stability On Structured PE Data Keywords Malware Detection; Portable Executable; Random Forest; Neural Network; Machine Learning