Machine Learning Techniques for Malware Detection

Abstract

Malware is one of the major threats to modern computer systems, mobile devices, cloud platforms, and networks. Traditional signature-based antivirus systems are effective against known malware but often have difficulty detecting new, polymorphic, obfuscated, and zero-day variants. Machine Learning (ML) provides a data-driven approach in which models learn patterns that distinguish malicious software from legitimate software. This paper presents an overview of ML techniques used for malware detection, focusing on static, dynamic, and hybrid analysis. Various algorithms, including Decision Tree, Random Forest, Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Naïve Bayes, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), are discussed. A general ML-based malware detection methodology involving dataset collection, preprocessing, feature extraction, feature selection, classification, and evaluation is presented. Important evaluation measures such as accuracy, precision, recall, F1-score, and false-positive rate are also described. The paper further examines applications in endpoint security, Android malware detection, network security, cloud computing, and IoT environments. Major challenges include dataset quality, class imbalance, concept drift, adversarial attacks, explainability, computational cost, and cross-dataset generalization. Finally, future research directions such as Explainable AI, federated learning, graph neural networks, transformer models, continuous learning, and lightweight edge-based detection are discussed.