Snort Meets Transformers: Accelerating Transformer-Based Network Traffic Classification for Real-Time Performance
Mohamed Hashim Changrampadi, Magnus Almgren, Pablo Picazo‐Sanchez, Ahmed Ali-Eldin · 2025
Transformer-based models have emerged as a powerful solution for network traffic classification, achieving high accuracy by autonomously learning patterns in raw traffic data. However, their high computational costs make real-time deployment impractical. In contrast, industry-proven tools like Snort and Suricata offer efficient network analysis but rely on manually crafted signatures, resulting in slower updates and limited adaptability to emerging threats.