Tags → #encrypted traffic classification

Recent research into MH-Net has introduced advancements in classifying encrypted network traffic, utilizing byte correlation analysis and multi-task training techniques to improve real-time monitoring and reduce false positives.

A recent study presents MERLOT, a scalable mixture-of-expert model designed for the efficient classification of encrypted traffic while reducing computational costs.