The rapid evolution of cyber threats has rendered traditional rule-based security mechanisms increasingly inadequate. As cyber attacks grow in complexity and scale, there is a pressing need for intelligent and adaptive security solutions. Machine Learning (ML) has emerged as a powerful tool in the realm of cyber security, offering the ability to detect anomalies, recognize patterns, and predict potential threats with greater accuracy and speed. This paper exploresthe implementation and effectiveness of machine learning-based threat detection systems, examining both supervised and unsupervised learning techniques in identifying malware, phishing, intrusion attempts, and other cyber threats. The study also discusses the challenges associated with data quality, model interpretability, adversarial attacks, and real-time deployment. Through a comprehensive review of current approaches and future directions, this paper highlights how ML is shaping the next frontier of proactive and dynamic cyber defense systems.
Cyber Security, Machine Learning, Threat Detection, Intrusion Detection Systems (IDS), Anomaly Detection, Malware Detection, Artificial Intelligence, Network Security, Phishing, Cyber Threat Intelligence.