This article provides in-depth research on artificial intelligence techniques for real-time network traffic analysis, with a focus on cybersecurity threat identification and mitigation. The study carries out a thorough comparison of deep learning, reinforcement learning, and conventional machine learning methods over a ten-year period. Our approach mixes real-world network traffic statistics from enterprise settings with synthetic phony data. Evaluation metrics include things like detection accuracy, false positive rates, and real-time system latency. This study uses more than 50 peer-reviewed references from prestigious publications and conference proceedings, and it follows the IEEE academic format. The results provide important insights into the advantages and disadvantages of different AI-driven approaches. With an emphasis on cybersecurity threat identification and mitigation, this article presents extensive research on artificial intelligence approaches for real-time network traffic analysis. Over the course of a decade, the study conducts a comprehensive comparative analysis of deep learning, reinforcement learning, and traditional machine learning techniques. Our method combines fake data synthesis with realworld network traffic statistics from enterprise settings.
Artificial Intelligence (AI), Machine Learning, Reinforcement Learning, Cybersecurity threat identification