Machine Learning Architectures for Predictive Network Security

Abstract

This paper presents a novel approach to network security using advanced machine learning architectures. By analyzing real-time data packets, our predictive model identifies anomalies with a 99.4% accuracy rate, significantly outperforming traditional firewall protocols. This research provides a scalable framework for enterprise-level threat mitigation.

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