title	emoji	colorFrom	colorTo	sdk	app_port
HydraDragon Analysis Dashboard	🐉	green	blue	docker	7860

HydraDragon SDK & Analysis Dashboard

This repository contains the HydraDragon Machine Learning SDK and a web-based dashboard for analyzing PE files.

Features

• GitHub Release Sync: Downloads and processes .7z archives from the latest GitHub Release.
• PE File Analysis: Extracts features from PE files using the antivirus_sdk_cli.
• Manual Classification: A web UI to label files as 'Benign' or 'Malicious'.
• ML-Powered Scanning: Train a RandomForest model on your labeled data and use it to automatically classify new files.

Cloud Deployment (Hugging Face Spaces)

This application is configured for deployment on Hugging Face Spaces, a free platform for hosting ML applications.

Deployment Steps:

1. Clone the Space:

   git clone https://huggingface.co/spaces/HydraDragonAntivirus/HydraDragon-Dashboard

   • When prompted for a password, use an access token with write permissions. Generate one from your settings: huggingface.co/settings/tokens

2. Install hf CLI (if not already installed):

   powershell -ExecutionPolicy ByPass -c "irm https://hf.co/cli/install.ps1 | iex"

3. Download the Space (Optional, if you cloned directly):

   hf download HydraDragonAntivirus/HydraDragon-Dashboard --repo-type=space

4. Add your application files: Ensure your requirements.txt, app.py, and Dockerfile are in the root of your cloned repository. These files have already been prepared for you.

   • requirements.txt:

     pefile
     capstone
     numpy
     pandas
     scikit-learn
     joblib
     tqdm
     requests
     fastapi
     uvicorn[standard]
     

   • app.py: Your FastAPI application logic.

   • Dockerfile:

     # Read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
     FROM python:3.9
     RUN useradd -m -u 1000 user
     USER user
     ENV PATH="/home/user/.local/bin:$PATH"
     WORKDIR /app
     COPY --chown=user ./requirements.txt requirements.txt
     RUN pip install --no-cache-dir --upgrade -r requirements.txt
     COPY --chown=user . /app
     CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

5. Commit and Push:

   git add .
   git commit -m "Update application for Hugging Face Spaces deployment"
   git push

   Your Space should be running on its Hugging Face page after a few moments! It will listen on port 7860.

How to Use the Dashboard

1. Access the Dashboard: Once deployed on Hugging Face Spaces, your dashboard will be available at the public URL provided by Hugging Face (e.g., https://huggingface.co/spaces/YourUsername/YourSpaceName).
2. Sync Files: Click the Sync from GitHub button to fetch the latest release assets. New PE files will be added to the database as "Unlabeled".
3. Label Files: Manually label some files as "Benign" or "Malicious". You need a good number of both to train an effective model.
4. Train Model: Once you have enough labeled data, click the Train Model button. This will create a packed_detector.joblib file within the Space's storage.
5. Scan Files: For any remaining "Unlabeled" files, click the Scan with ML button to automatically classify them using your newly trained model.

Folder Scanner CLI

You can use the joblib_folder_scanner.py script to scan a specified folder for PE files and classify them using the trained packed_detector.joblib model.

Prerequisites: Ensure all dependencies are installed:

pip install -r requirements.txt

Usage: To scan a folder, run the following command, replacing D:\yenindirme\2025-11-28 with the path to the folder you want to scan:

python scanners/joblib_folder_scanner.py --folder "D:\yenindirme\2025-11-28" --model packed_detector.joblib

The script will output the prediction (Malicious/Packed or Benign/Unpacked) and the associated probability for each PE file found in the specified folder.