A Broad Review of Machine Learning-Driven Approaches for Detecting and Mitigating Cyber Security Threats

The increasing sophistication and volume of cyber threats pose significant challenges to traditional defense mechanisms, necessitating more adaptive and intelligent solutions. This survey critically reviews machine learning (ML)-driven approaches for cybersecurity threat detection and mitigation, focusing on the limitations of current supervised and semi-supervised methods. While effective for known attacks, these models often fail to detect zero-day exploits, struggle with high-dimensional network data, and depend heavily on labeled datasets, which are often incomplete or insecure. To address these challenges, this review explores advanced unsupervised learning techniques such as deep clustering, autoencoders, and generative adversarial networks (GANS) that can extract hidden patterns from unlabeled traffic and improve anomaly detection. The paper’s key contribution lies in identifying research gaps and proposing a novel direction: integrating unsupervised learning with federated and reinforcement learning frameworks to build scalable, privacy-preserving, and adaptive security systems. This study highlights the potential of such integration to enable proactive and resilient cyber defense mechanisms, offering a significant advancement in intelligent cybersecurity solutions.