Machine Learning-Based Malware Detection and Classification Techniques

The continuous evolution of malware has forced cybersecurity professionals and academic researchers to explore advanced methods for detection and classification. This paper examines the application of machine learning (ML) techniques—specifically supervised learning algorithms such as Support Vector Machines (SVM), Random Forest (RF), and Neural Networks—to diagnose and mitigate malware threats, particularly on Windows-based environments. Emphasis is placed on the diagnostic applications of these methods, ethical concerns raised by the integration of ML into cybersecurity, and the future implications of deep learning-based systems. Drawing on current research, the paper discusses how deep learning and traditional ML models can be integrated to improve classification accuracy, while also addressing issues related to data privacy, algorithmic bias, and accountability. The experimental results synthesized from recent studies provide a comprehensive overview of performance metrics achieved by these models, confirming that deep learning techniques significantly enhance malware classification accuracy. The discussion is further extended to potential adversarial attacks and the implementation of explainable AI techniques to improve transparency in decision-making. This paper is aimed at cybersecurity professionals and researchers in machine learning, offering an in-depth analysis of current methods and proposing future research directions to build more robust, ethical, and efficient malware detection frameworks.