Spaces:
Sleeping
Sleeping
File size: 3,390 Bytes
ce998c6 d06d82c 0417a8c d06d82c 0417a8c d06d82c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 |
---
title: NetShield – ML Model for Network Attack Detection
emoji: 🛡️
colorFrom: gray
colorTo: red
sdk: gradio
sdk_version: "5.4.0" # latest stable gradio
app_file: app.py
pinned: false
license: mit
---
# NetworkAttackDetector — Real‑time Network Threat Detection 🛡️
Lightweight Gradio app to predict and explain potential network scan types from request metadata. Built with a scikit‑learn pipeline, ready to run locally. 🚀
---
## Badges
[](https://www.python.org/)
[](./LICENSE)
---
## Table of Contents
- [Demo](#demo)
- [Features](#features)
- [Installation / Setup](#installation--setup)
- [Usage](#usage)
- [Configuration / Options](#configuration--options)
- [Contributing](#contributing)
- [License](#license)
- [Acknowledgements / Credits](#acknowledgements--credits)
---
## Demo
The repository includes real demo assets under `./demo/`.
- Image preview:
- Short walkthrough (MP4):
<video src="./demo/demo.mp4" controls width="640"></video>
---
## Features
- **Interactive UI** using `gradio` via `ui.py` → `build_ui()`.
- **Pretrained artifacts** loaded from `./models/` via `utils.py` → `load_artifacts()`.
- **Fast inference** using a serialized scikit‑learn pipeline (`joblib`).
- **Human‑readable prediction** by decoding labels with a fitted encoder.
---
## Installation / Setup
Use a virtual environment for isolation.
```bash
# Create a virtual environment
python -m venv .venv
# Activate it
# On Linux/Mac:
source .venv/bin/activate
# On Windows:
.venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
```
---
## Usage
Run the Gradio app from the project root:
```bash
python app.py
```
Then open the local URL printed by Gradio (e.g., http://127.0.0.1:7860/).
### Input fields
- `Port` (number)
- `Request Type` (e.g., GET, POST)
- `Protocol` (e.g., HTTP, HTTPS)
- `Payload Size` (number)
- `User Agent` (string)
- `Status` (e.g., 200, 404)
### Output
- A predicted scan type (human‑readable label) returned by `predict_intrusion()`.
---
## Configuration / Options
- **Model artifacts**: The app expects the following files to exist:
- `models/network_logs_pipeline.joblib`
- `models/target_encoder.joblib`
- **Changing artifact paths**: Update `utils.py` → `load_artifacts()` if your models live elsewhere.
- **Environment**: Ensure your Python version satisfies the version in the badge above.
---
## Contributing
Contributions are welcome! Suggestions, issues, and PRs help improve the project.
1. Fork the repo
2. Create a feature branch: `git checkout -b feat/your-feature`
3. Commit changes: `git commit -m "feat: add your feature"`
4. Push to branch: `git push origin feat/your-feature`
5. Open a Pull Request
Please keep PRs focused and include context, screenshots when relevant, and tests if logic changes.
---
## License
This project is licensed under the MIT License — see [LICENSE](./LICENSE) for details.
---
## Acknowledgements / Credits
- Model training and experimentation are illustrated in `network-threat-detection-with-f1-99.ipynb` using `pandas`, `numpy`, `scikit‑learn`, `xgboost`, and `shap`.
- UI is built with `gradio` and model artifacts are loaded with `joblib`.
|