Graph-Based Contrastive Learning and Clustering for Open-World Encrypted Traffic Classification

This research proposes GCLC (Graph-based Contrastive Learning and Clustering), a framework for classifying encrypted network traffic (data sent over networks in coded form) in open-world scenarios where unknown traffic types may appear. The system uses Graph Neural Networks (machine learning models that work with interconnected data) and special learning techniques to identify traffic patterns even when data is imbalanced or new, achieving 95% accuracy at recognizing previously unseen traffic classes.