EXECUTIVE SUMMARY

The integration of Generative AI and Large Language Models (LLMs) into network security and operational management presents transformative opportunities for enhancing application identification and traffic classification. Traditional methods that rely on literal matching and regex-based software flows face limitations in handling complex network tasks, particularly in deep packet inspection (DPI) and encrypted traffic analysis. This research proposes a hybrid architecture that leverages multiple LLM backends, such as GPT-2 and ModernBERT, to facilitate dynamic, context-aware, and adaptive traffic classification systems. While earlier works like TrafficGPT demonstrated the potential of LLMs in encrypted traffic classification, our research advances these capabilities by integrating batch processing and optimized edge inference techniques—directly addressing the scalability and performance constraints of prior approaches. We conducted comprehensive evaluations, including workload characterization and hardware-specific optimizations on Intel® Xeon® processors and Intel® Arc™ A770 Graphics. These efforts establish a practical foundation for deploying LLM-based systems at scale, extending the frontier of traditional network security.