# Applying JEPA-Style Predictive Learning to JA4-Derived Network Fingerprints

arXiv: https://arxiv.org/abs/2607.08465 · HTML: https://mlsanity.com/paper/2607.08465/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 9 Jul 2026.

## Builds on (most cited first, 4 of 4)

- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [Revisiting Feature Prediction for Learning Visual Representations from Video](https://mlsanity.com/paper/2404.08471/) (2024)
- [ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification](https://mlsanity.com/paper/2202.06335/) (2022)
- [Generic Multi-modal Representation Learning for Network Traffic Analysis](https://mlsanity.com/paper/2405.02649/) (2024)

## Abstract (arXiv)

I-JEPA and V-JEPA learn by matching latent predictions to target encoder outputs rather than regenerating the original input, and this has worked well for images and video. We explore whether the same objective works for compact network fingerprints. We built JA4-JEPA, a Transformer-based model trained on JA4, JA4H, JA4S, and JA4X subfields drawn from JA4DB and CIC-IDS- 2017. The training data combines roughly 397K samples from both sources, though no single sample contains all four view families. We evaluated the learned representations with a frozen kNN probe on protocol-family classification across TLS, DNS, and SSH. On 39,416 heldout samples the model achieved a cosine similarity of 0.9899 and a kNN accuracy of 0.9220. These results indicate that JEPA-style predictive learning can produce useful embeddings from JA4-derived fingerprints, even with incomplete view overlap across sources. Keywords: JA4, network fingerprinting, JEPA, predictive representation learning, self-supervised learning