Comparison of Supervised Machine Learning Approaches for Enhanced File Malware Detection: Techniques and Evaluations

This paper explores the use of Machine Learning (ML) techniques in enhancing malware detection amidst the evolving landscape of cyber threats. Traditional signature-based methods are becoming increasingly ineffective against sophisticated malware employing evasion tactics like polymorphism and metamorphism. Our study introduces a taxonomy of ML-based malware detection models, including supervised, unsupervised, and reinforcement learning approaches. We conduct an evaluation of these models on various datasets to determine their effectiveness in identifying malicious code. The paper discusses the strengths, weaknesses, and security concerns of these ML models, particularly their vulnerability to adversarial attacks. Among the techniques evaluated, basic supervised learning algorithms, such as Decision Trees and Random Forests, emerged as the most effective. These algorithms demonstrated superior performance in detecting known malware types, characterized by high accuracy, ease of implementation, and efficient processing times compared to more complex models. Our findings offer insights for developing more resilient and intelligent malware detection systems and address the challenges of integrating these models into real-world cybersecurity systems.