Quick Start

Prerequisites

Before deploying Soteria, ensure you have the following installed:

• Python 3.13+
• Node.js (v18+) and npm
• Git

1. Backend & Intelligence Engine Setup

The backend handles AST analysis, machine learning inference, and user authentication.

1. Clone the repository:

   git clone https://github.com/manu-j3400/A.C.I.D.git
   cd A.C.I.D
   

2. Configure the Python environment:

   cd backend
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt
   

3. Initialize the Database: The system uses SQLite for local development. Simply running the app for the first time will generate the database and seed a default admin account.

   python app.py
   

   • Default Admin: admin@kyber.io
   • Password: admin123

2. Model Training (Optional)

Soteria comes with pre-trained weights, but you can retrain the "Structural DNA" model using the provided pipeline if you have your own dataset.

# From the backend directory
python src/trainerModel_AST.py

This will process the data in /data, normalize it via AST, and export a new acidModel.pkl to the /ML_master directory.

3. Frontend Dashboard Setup

The "Cyber Sentinel" dashboard is a React application built with Vite and Tailwind CSS.

1. Install dependencies:

   cd ../frontend
   npm install
   

2. Configure Environment Variables: Create a .env file in the frontend folder:

   VITE_API_URL=http://localhost:5001
   

3. Launch the Development Server:

   npm run dev
   

   The dashboard will be available at http://localhost:5173.

4. Perform Your First Scan

Once both tiers are running, you can verify the pipeline:

1. Log in to the dashboard at http://localhost:5173/login.
2. Navigate to the Scanner tab.
3. Paste a Python function into the editor.
   • Try a clean function: def add(a, b): return a + b
   • Try a suspicious pattern: A function containing obfuscated getattr(os, 'sys' + 'tem') calls.
4. Click Analyze Code to view the structural breakdown and security label.

API Quick Reference

If you wish to integrate the Intelligence Engine into your own tools, use the /analyze endpoint:

Endpoint: POST /analyze
Authentication: Bearer Token (JWT)

Request Body:

{
  "code": "def malicious_payload():\n    import os\n    os.system('rm -rf /')"
}

Response:

{
  "prediction": 1,
  "confidence": 0.98,
  "label": "Malicious",
  "dna_sequence": {
    "Assign": 1,
    "Call": 2,
    "Import": 1
  }
}