predictive-learning

The paper establishes a theoretical connection between probabilistic Joint-Embedding Predictive Learning (JEPA) and Hidden Markov Models (HMMs), providing a state-space interpretation and introducing Markov-Chain JEPA for enhanced consistency.

This paper explores applying JEPA-style predictive learning to JA4-derived network fingerprints, building a Transformer-based model (JA4-JEPA) trained on JA4, JA4H, JA4S, and JA4X subfields. The model achieves strong performance on protocol-family classification, suggesting JEPA objectives can work for compact network fingerprint representations.