AI for Real-Time Network Traffic Analysis: Advanced Cybersecurity Threat Detection and Prevention Methods

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 real-world network traffic statistics from enterprise settings.