Two Heads Are Better Than One: Models-to-Model Learning for Encrypted Traffic Analysis

This research addresses a problem in encrypted traffic analysis (ETA, the process of identifying what data is being sent over the internet by examining encrypted network traffic patterns), where existing machine learning methods require lots of manually labeled training data. The authors propose Models-to-Model Learning (M2ML), a new approach that learns from existing ETA models instead of requiring labeled data, using a large language model to align different models' feature spaces (the variables they measure) and resolve disagreements between them based on credibility.