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InfoResearchPeer-reviewed
Graph-Based Contrastive Learning and Clustering for Open-World Encrypted Traffic Classification
- Published
- Record updated
Summary
Researchers propose GCLC, a Graph-based Contrastive Learning and Clustering framework for classifying encrypted network traffic in open-world settings. Encrypted flows are modeled as traffic interaction graphs and encoded by a Graph Neural Network trained with a hybrid loss, while a Mahalanobis distance-based detector finds unseen classes. Evaluated on 3 public datasets and 1 self-collected dataset, GCLC outperforms existing methods on 4 metrics, with 95.02% open-set new class recognition accuracy.