CyberBERT: Network Security Intelligence with Language AI and Real-time Power BI Analytics

We're living in a world where cyber threats are constantly evolving, creating an urgent need for smarter security tools. In this paper, we introduce CyberBERT, a new approach that brings the power of language AI to network security. Here's the cool part: we've figured out how to transform complex network data into something that language models can understand, letting us identify threats with remarkable accuracy. By converting 84 different network measurements into something resembling natural language, we've created a bridge between network security and the recent breakthroughs in AI language understanding. Our system achieves 96% accuracy in identifying six different types of network traffic (normal connections, DoS attacks, port scanning, and more), and it works incredibly fast—just 5 milliseconds on systems with a decent GPU and 40 milliseconds on regular computers. This is significantly better than traditional approaches, with accuracy improvements of over 3%. Throughout this paper, we'll walk you through how CyberBERT works, why transforming network data into text makes such a difference, and how we've optimized everything to run in real-time on standard hardware. What's particularly exciting is that our system can spot sophisticated attack patterns without requiring the extensive expert knowledge that traditional systems demand. Key Words: Network Security, Traffic Classification, Distilbert, Bert, Transformer Models, Intrusion Detection, Deep Learning, Real-time Analysis, Feature Transformation