A Review of Next-Generation Data-Driven Malware Detection Mechanisms

Abstract

The escalating sophistication of cyber threats has outpaced traditional malware detection methods, necessitating advanced approaches to safeguard information systems. This review explores next-generation malware detection mechanisms driven by data analytics, Machine Learning (ML), and Artificial Intelligence (AI). Unlike conventional signature-based methods, which rely on known patterns, data-driven techniques leverage large datasets and sophisticated algorithms to identify and respond to known and novel malware. We examine approaches, including supervised and unsupervised ML, deep learning, and hybrid methods integrating behavioral analysis with AI. Machine learning models, such as neural networks, offer enhanced detection capabilities but require substantial training data and computational resources. Unsupervised methods and anomaly detection provide flexibility in identifying previously unknown threats, though they may face challenges in distinguishing actual threats from benign anomalies. The review also discusses the effectiveness of dynamic and behavioral analysis techniques, such as sandboxing and runtime monitoring. We conclude with an overview of current challenges, including data privacy concerns and the need for scalable solutions, and suggest directions for future research further to improve the robustness and efficiency of malware detection systems.