Enhancing Cybersecurity with Machine Learning: A Comprehensive Review

Abstract

As cyber threats continue to evolve, the integration of machine learning (ML) in cybersecurity has emerged as a critical defense strategy. This paper provides a comprehensive review of ML applications in detecting and mitigating cyber threats. By analyzing various ML techniques, such as anomaly detection, classification, and clustering, the study assesses their effectiveness in identifying malicious activities, preventing data breaches, and securing digital infrastructures. The review highlights the strengths and limitations of ML-based cybersecurity solutions and offers insights into future research directions for enhancing their robustness and reliability.