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README.md

@@ -1,15 +1,64 @@
----
-title: Nids Deployment
-emoji: 🐠
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 6.14.0
-python_version: '3.13'
-app_file: app.py
-pinned: false
-tags:
-- ml-intern
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🛡️ Network Intrusion Detection System (NIDS)
+
+Real-time network intrusion detection using ML & Deep Learning models trained on the NSL-KDD dataset.
+
+## Models
+- **XGBoost** (best) — Accuracy 76.18%, AUC-ROC 95.75%
+- **Random Forest** — Accuracy 73.10%, AUC-ROC 95.34%
+- **MLP** — Deep learning baseline
+- **Autoencoder** — Unsupervised anomaly detection
+- **LSTM** — Sequential feature modeling
+- **Transformer** — FlowTransformer-style architecture
+
+## Dataset
+[NSL-KDD](https://huggingface.co/datasets/Mireu-Lab/NSL-KDD) — 185K records, 41 features, binary classification (normal/anomaly)
+
+## Deployment
+
+### REST API (FastAPI)
+```bash
+pip install fastapi uvicorn
+uvicorn api:app --host 0.0.0.0 --port 8000
+```
+
+Endpoints:
+- `GET /health` — Health check
+- `GET /models` — List models
+- `POST /predict` — Single flow detection
+- `POST /predict/batch` — Batch detection
+- `GET /stats` — Usage statistics
+
+### Gradio UI
+```bash
+python app.py
+```
+
+## API Example
+```bash
+curl -X POST http://localhost:8000/predict \
+  -H "Content-Type: application/json" \
+  -d '{"features":[0,1,45,0,491,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,2,2,0,0,0,0,1,0,0,0,0.17], "model":"XGBoost"}'
+```
+
+## Results
+| Model | Accuracy | Macro F1 | AUC-ROC | Type |
+|-------|----------|----------|---------|------|
+| XGBoost | 76.18% | 76.04% | 95.75% | Supervised |
+| RandomForest | 73.10% | 73.05% | 95.34% | Supervised |
+| MLP | 73.28% | 73.21% | 89.33% | Supervised |
+| Autoencoder | 71.84% | 71.34% | 73.60% | Unsupervised |
+
+## Project Structure
+```
+nids_project/
+├── api.py           # FastAPI REST API
+├── app.py           # Gradio web UI
+├── space_app.py     # HF Spaces app
+├── data_loader.py   # Dataset loading
+├── ml_models.py     # ML implementations
+├── dl_models.py     # DL implementations
+├── realtime.py      # Real-time pipeline
+├── realtime_batch.py # Batch throughput benchmark
+├── Dockerfile       # Container image
+└── requirements.txt # Dependencies
+```