🔐 cyberattack-detector-AI

A FastAPI-based AI web application for real-time cyberattack detection using a trained machine learning model. It provides an interactive web interface for users to input network traffic features and receive live predictions on whether the traffic is Benign or an Attack.

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🚀 Features

• ✅ Built with FastAPI for high-speed performance
• 🔍 Predicts attacks using a trained scikit-learn model
• 📈 Uses a StandardScaler for input feature normalization
• 🧠 Dynamic input form generated from features.pkl
• 💡 User-friendly web interface with Jinja2 templates
• 🧪 Robust error handling and form validation

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📁 Project Structure

cyberattack-detector-AI/
│
├── main.py               # FastAPI backend
├── model.pkl             # Trained ML model
├── scaler.pkl            # Fitted StandardScaler
├── features.pkl          # List of input features
├── templates/
│   └── index.html        # Web form and result display
├── static/               # CSS or image assets (optional)
└── README.md             # This file

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⚙️ Installation & Setup

1. Clone the repository

   git clone https://github.com/your-username/cyberattack-detector-AI.git
   cd cyberattack-detector-AI

2. Install required packages

   pip install -r requirements.txt

3. Run the application

   uvicorn main:app --reload

4. Access the app Open your browser and go to:
   👉 http://127.0.0.1:8000

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📊 How It Works

1. Loads the ML model, scaler, and feature list.
2. Renders a form with all required feature fields.
3. On form submission:
   • Values are collected and scaled.
   • The model makes a prediction.
   • The result is displayed as Benign or Attack.

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✅ Requirements

• Python 3.8+
• FastAPI
• Uvicorn
• scikit-learn
• joblib
• Jinja2

Install all requirements:

pip install fastapi uvicorn scikit-learn joblib jinja2

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📷 Screenshot

(Add your UI screenshot here once available)
Example:

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📜 License

This project is licensed under the MIT License

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👨‍💻 Author

Muhammad Saeed
AI & Machine Learning & IoT Enthusiast