Machine Learning in Cybersecurity: A Comprehensive Review of Threat Detection, Prevention, and Response Strategies

The increased complexity and rate of cyberattacks have necessitated a change in the paradigm of cybersecurity strategies. The conventional rule-based systems are increasingly insufficient to address the dynamic and adaptive behaviour of modern threats. Machine learning (ML), with its ability to learn from vast amounts of data and identify complex patterns, has emerged as a powerful ally in combating these threats. This paper offers a comprehensive review of the application of ML in cybersecurity, its applications across the entire threat lifecycle. We present a wide range of ML approaches, including supervised, unsupervised, and deep learning approaches, and their effectiveness in areas of high consequence like intrusion detection, malware analysis, phishing detection, and anomaly detection. The review also provides ML-based prevention techniques, including vulnerability assessment and access control, and response techniques, including automatic incident response and threat intelligence. The paper also provides the challenges and limitations of applying ML in cybersecurity, including adversarial attacks, data quality issues, and explainability.