Machine learning (ML) has emerged as a transformative tool in cybersecurity, particularly for automating threat detection processes that traditionally depend on manual analysis. By leveraging algorithms such as convolutional neural networks (CNNs), support vector machines (SVMs), and Bayesian classifiers, ML enables more efficient identification of malicious activities compared to human-driven approaches. However, the application of ML in security contexts faces distinct challenges, including adversarial evasion tactics and the need for interpretable decision-making frameworks. Recent advancements focus on extracting latent patterns from network traffic data to train adaptive models capable of preempting attacks like ransomware and advanced persistent threats (APTs). This review evaluates ML-driven methodologies for securing digital infrastructures, analyzing their efficacy against modern cyberattacks, and addressing limitations such as dataset bias and concept drift. Furthermore, it investigates shifts in attack vectors over the past decade, offering insights into how data-driven models can counteract evolving malware strategies that endanger global networked systems.
