Malware Detection System

Abstract

This research presents a lightweight malware detection system for Microsoft Windows Portable Executable (PE) files using static analysis and supervised machine learning. The study addresses the limitations of signature-based antivirus and the operational overhead of dynamic sandboxing by extracting discriminative yet inexpensive static features—average section entropy, number of sections, total raw section size, and file size—and training a Random Forest classifier to detect malicious executables. The methodology follows Design Science Research and CRISP-DM, covering requirement specification, a modular architecture, implementation in Python, dataset curation, model training, and comprehensive evaluation.