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As network security threats continue to evolve in complexity and sophistication, there is a growing need for advanced solutions to enhance network security and threat detection capabilities. Machine learning (ML) has emerged as a powerful tool in this context, offering the potential to detect and mitigate threats in real-time by analyzing vast amounts of network data. This comprehensive review explores the role of machine learning in enhancing network security and threat detection. The review begins by providing an overview of the current landscape of network security threats and the challenges faced by traditional security approaches. It then delves into the fundamental principles of machine learning and its application to network security. Various machine learning techniques, including supervised learning, unsupervised learning, and deep learning, are discussed in detail, highlighting their strengths and limitations in the context of threat detection. Next, the review examines the application of machine learning in different aspects of network security, including intrusion detection, malware detection, anomaly detection, and behavioral analysis. Case studies and real-world examples are presented to illustrate the effectiveness of machine learning-based approaches in identifying and mitigating security threats. Furthermore, the review discusses the challenges and considerations associated with deploying machine learning in network security environments, such as data privacy, model interpretability, and adversarial attacks. Strategies for addressing these challenges and improving the robustness of machine learning models are explored. Finally …