Paper Title
ANALYZING THE ROLE OF MACHINE LEARNING IN ENHANCING NETWORK SECURITY PROTOCOLS

Abstract
Due to the increasing of dynamic cyber threats, the paper delves into the space of the synthesis of machine learning (ML) techniques into conventional network security technology of dynamic cyber threats, the paper delves into the space of the synthesis of machine learning (ML) techniques into conventional network security technology. In this work, the study examines ML's influence on traditional protocols, suitability to new attacks, and associated risks to give a clear view on how intrusion detection and prevention systems can be reinforced with ML. A systemic literature review and content analysis are carried out by the study to highlight the critical components and challenges associated with integrating ML with an aim of providing useful scalable prototypes and resources. Discoveries unveil the potential ML has to bolster cyber security; however, the study points at the tradeoffs and security loopholes that require strict safety measures. Future research should tackle the issue of scalability, resource pressure, and security threats, so as to better improve the ML-based security systems. Keywords - Machine Learning, Network Security Protocols, Cybersecurity, Intrusion Detection, Adversarial Attacks.