Cyber Threat Analysis using Machine Learning for Proactive Mitigation: A Comprehensive Review

With the rapid increase of digital connectivity we have seen some extraordinary opportunities as well as an ominous escalation of cyber threats. Conventional rule-based approaches typically fail to identify the dynamic sophisticated cyber-attacks. As a result, machine learning (ML) has become a powerful tool in forward security for pro-active defense against cyber threats to detect anomaly, anticipation of an attack, and tampering by evolving threats. This review study provides an overview of the field of cyber-threat analysis using machine learning, and an in-depth analysis of a variety of supervised, unsupervised, and deep learning models employed in cybersecurity. It classifies some important cyber threats, analyzes popular datasets like CICIDS2017 and UNSW-NB15, and evaluates the ML models effectiveness using accuracy, F1-score, and detection rate performance measures. Additionally, the paper discusses emerging challenges, including but not limited to, imbalanced data, model interpretability, and adversarial evasion, as well as potential future directions, such as federated learning and explainable AI in this field. The paper tries to connect the divide between the cybersecurity practitioners and the ML researchers by giving an organized survey of the state of the art and suggesting the directions in which the path to developing intelligent and resilient security systems.