Malware remains one of the most significant threats to modern computer and digital systems. Owing to its continuously evolving nature, preventing and detecting malware has become increasingly complex. Traditional rule-based detection techniques are often ineffective against novel or obfuscated malware variants. Consequently, recent research has focused on leveraging machine learning and deep learning methods to identify evolving malicious patterns that can detect previously unseen threats. Since 2015, there has been a substantial growth in studies applying artificial intelligence approaches for malware detection and classification. This paper presents a contemporary review of state-of-the-art machine learning and deep learning algorithms used in malware analysis. To ensure methodological rigor and transparency, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was adopted. The review critically evaluates the strengths and limitations of existing models, analyses their performance in detecting and classifying diverse malware types, and discusses emerging challenges such as dataset imbalance, adversarial attacks, and model generalizability. Finally, the paper outlines key research directions aimed at improving the robustness, scalability, and interpretability of AI-driven malware detection systems.
Redhu et al. (Tue,) studied this question.
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