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kritikahd007/
net-jepa

Name: net-jepa
Availability: InStock

pytorch

joblib

network-traffic-classification

encrypted-traffic

self-supervised-learning

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Net-JEPA — Context-Aware Flow Embeddings for Encrypted Network Traffic

Net-JEPA is a Joint-Embedding Predictive Architecture (JEPA) for encrypted network flows. It classifies traffic into 8 common traffic types from the shape of the traffic — packet sizes, inter-arrival timing, and direction — without decrypting any payload. It is trained from scratch (no foundation / pre-trained / closed-weight model is used anywhere).

Code / full docs: https://github.com/Stinson-83/Net-JEPA
Team: FlowState (Archisman Dhar, Kritik Gupta), IIT Kanpur — Samsung EnnovateX 2026, PS #2
License: Apache-2.0

What's in this repository

File	What it is
`net_jepa_phase3.pt`	Trained Net-JEPA checkpoint (Phase 3, epoch 50) — encoder + fusion + downstream embedding head, with α-centering baked in (~1.76 M params)
`knn.joblib`	Fitted cosine k-NN (k=5) traffic-type classifier over the labelled embeddings
`labels.json`	The 8 traffic-type names + `type2id` mapping
`config.yaml`	The exact `traffic.yaml` hyper-parameters used to build the model
`umap.joblib`	(optional) Fitted UMAP reducer (128-D → 2-D) for the Signal Atlas visualization — not needed for classification

The checkpoint produces a 128-D L2-normalised embedding per flow; the k-NN reads that embedding to predict one of 8 traffic types.

Model details

Input: a flow's first ≤64 packets as a (64 × 9) tensor [size/1500, log1p(IAT), signed direction, protocol one-hot×4, rtt_norm, rtt_flag] + a (15,) flow-context vector (durations, flag ratios, per-capture host stats, RTT) + a (64,) padding mask.
Encoder: 4-layer Temporal Transformer (d=128, 4 heads) + cross-attention fusion with the flow-context vector.
Self-supervised pretraining: masked-latent prediction against an EMA target branch with a VICReg loss (no labels, no negatives).
Supervised sharpening: traffic-type-level SupCon on the kept embedding, then α-centering (common-mode removal, α≈0.65) to isotropise the space.
Classifier: cosine k-NN (k=5).
Size / speed: ~1.76 M parameters; ~3.5 ms / flow on CPU (no GPU needed to serve).

The eight traffic types

audio_streaming · cloud_gaming · live_streaming · metaverse · online_gaming · video_conferencing · video_on_demand · web_browsing.

Evaluation (held-out test split)

Benchmark KPI	Target	Achieved
Intra-class cosine	> 0.7	0.98
Inter-class cosine	< 0.3	−0.04
Classification accuracy	≥ 90%	99.7%
Generalization (few-shot, η=7)	≥ 85%	99.6%
Real-time latency / flow	< 100 ms	3.5 ms (CPU)

macro-F1 0.992 · silhouette 0.87. Per-class F1 ranges 0.971 (cloud gaming, the rarest / hardest) to 1.000 (metaverse). Full numbers: see the repo's docs/results.md.

How to use

The checkpoint needs the netjepa code to instantiate the architecture.

bash

git clone https://github.com/Stinson-83/Net-JEPA && cd Net-JEPA
pip install -r requirements.txt && pip install -e .
pip install huggingface_hub

python

import joblib, json, torch
from huggingface_hub import hf_hub_download
from netjepa.model.netjepa import NetJEPA
from netjepa.utils.io import load_checkpoint

REPO = "<your-username>/net-jepa"   # this repo id
ckpt   = hf_hub_download(REPO, "net_jepa_phase3.pt")
knn    = joblib.load(hf_hub_download(REPO, "knn.joblib"))
labels = json.load(open(hf_hub_download(REPO, "labels.json")))["traffic_types"]

model = NetJEPA()
load_checkpoint(model, None, ckpt, torch.device("cpu"))
model.eval()

# pkt: (1,64,9) float, ctx: (1,15) float, mask: (1,64) bool
emb = model.forward_downstream(pkt, ctx, mask).detach().cpu().numpy()  # (1,128)
traffic_type = labels[int(knn.predict(emb)[0])]

For end-to-end .pcap inference (parse → flow → embed → classify → 2-D projection), use the terminal tool python -m netjepa.scripts.infer_pcap your.pcap --checkpoint <ckpt> --labels labels.json or the FastAPI server (src/server/app.py) — see docs/usage.md. The flow-building and feature code is identical to training (including per-capture host stats), so real captures classify correctly (e.g. a browser YouTube capture → video_on_demand).

Training data

All datasets are public; the current model trains on 8 traffic types, all fully supervised:

Kaggle · 5G Traffic Datasets — primary train/test (license "Unknown" → used under Kaggle terms, not redistributed).
Zenodo · VLC / Valencia (CC-BY-4.0) — full VLC set: Spotify → audio_streaming, Web → web_browsing, Netflix/Prime/YouTube → video_on_demand, Roblox → metaverse, Teams → video_conferencing.
Kaggle · Cloud Gaming Network Telemetry (BSD-3) — Xbox Cloud over 5G → cloud_gaming.

28,892 flows → 20,224 train (pretrain = downstream) / 8,668 test (30%) (leak-free stratified split, full supervision).

Intended use & limitations

Intended: QoS / 5G-slice prioritisation, traffic analytics, and anomaly triage on encrypted flows where DPI is impossible. Operates on metadata only.
Out of scope: identifying individual users or payload content (it cannot — only flow shape is modelled).
Known limitation (reported honestly): classification relies on per-capture host-behaviour features, so a pcap containing only a single flow yields degenerate host stats and may misclassify. Real captures contain many flows and work; multi-flow captures of the 8 trained types — including out-of-domain browser QUIC — classify correctly. See docs/results.md.

Citation

bibtex

@misc{netjepa2026,
  title  = {Net-JEPA: Context-Aware Flow Embeddings for Adaptive AI-based Network Traffic Classification},
  author = {Dhar, Archisman and Gupta, Kritik},
  year   = {2026},
  note   = {Samsung EnnovateX 2026, Problem Statement 2},
  url    = {https://github.com/Stinson-83/Net-JEPA}
}

Conceptually inspired by I-JEPA (Assran et al., 2023), VICReg (Bardes et al., 2022), SupCon (Khosla et al., 2020), and DANN (Ganin & Lempitsky, 2015); adapted to encrypted network-flow classification with original contributions (packet-shape encoder + RTT/context fusion, α-centering, traffic-type SupCon, and per-capture train/inference-consistent host stats).

kritikahd007/net-jepa