CyberSafe IDS

CyberSafe IDS is an intelligent, user-friendly Intrusion Detection System that leverages machine learning to analyze network traffic and detect anomalies in real time.
It features a modern web interface, user management, statistics, and actionable recommendations for network security.

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Table of Contents

• Features
• Tech Stack
• Screenshots
• Project Structure
• Quickstart
• Usage
• Configuration
• For Developers
• Security Notes
• Support
• License
• Collaborators

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🚀 Features

• Real-time Network Traffic Analysis
• Machine Learning-based Detection (XGBoost, 41 features)
• User Authentication & Roles (admin/user)
• Detailed Statistics & Analytics
• Profile Management with Image Upload
• Dark Mode Support
• Responsive, Modern UI (Tailwind CSS)
• Actionable Security Recommendations

🧰 Tech Stack

• Python 3 (core programming language)
• Flask (web framework)
• scikit-learn (machine learning & preprocessing)
• XGBoost (model training)
• MySQL (database)
• Tailwind CSS (frontend styling)
• HTML5 & Jinja2 (front end structure)
• JavaScript (frontend interactivity)
• Joblib (model serialization)
• Werkzeug (security, password hashing)
• Pillow (image processing for profile uploads)

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🖼️ Screenshots

Home Page

Alt: Home page showing navigation bar, hero section, and quick links.

Statistics Page

Alt: Dashboard with charts and tables showing detection statistics, normal vs anomaly counts, and user activity.

Login Page

Alt: Login form for existing users with username and password fields.

🛠️ Project Structure

.
├── app.py
├── requirements.txt
├── models/
│   └── xgboost_best_model.pkl
├── dataset/
│   └── Train_data_Dataset.csv
├── templates/
│   ├── index.html
│   ├── predict.html
│   ├── result.html
│   ├── statistics.html
│   ├── signup.html
│   ├── login.html
│   ├── profile.html
│   └── admin_users.html
├── static/
│   ├── uploads/
│   └── images/
└── README.md

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⚡ Quickstart

1. Clone the repository:

   git clone <your-repo-url>
   cd <your-project-folder>

2. (Recommended) Create and activate a virtual environment:
   • On Windows:

     python -m venv venv
     venv\Scripts\activate

   • On macOS/Linux:

     python3 -m venv venv
     source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

   (All required Python packages are listed in requirements.txt.)
4. Ensure MySQL server is running.
   • Default config: user root, password '', host localhost
   • Update credentials in app.py if needed.
5. Place your trained model file:

   models/xgboost_best_model.pkl
   

6. Run the app:

   python app.py

7. Open your browser:

   http://localhost:5000
   

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📝 Usage

• Sign up for a new account.
• Log in and access the detection analysis page.
• Fill in all 41 features (use dropdowns for categorical fields).
• Submit to analyze traffic and view results.
• View statistics and manage your profile.

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⚙️ Configuration

• MySQL Credentials:
  Edit mysql_config in app.py if your MySQL user/password is different.
• Model File:
  Must match the 41-feature input and label encoding as defined in app.py.

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🧑‍💻 For Developers

• Model Training:
  Use your own notebook to train and export the model as xgboost_best_model.pkl.
• Feature Engineering:
  Ensure the order and encoding of features matches between training and app.py.

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🛡️ Security Notes

• All user passwords are securely hashed.
• Only admins can access user management.
• All new signups are assigned the "user" role by default.

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📞 Support

• Email: abdukadir.sharif@just.edu.so
• Phone: +252 617273320

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📄 License

This project is for educational and research purposes.
Contact the author for commercial or production use.

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👥 Collaborators

• Abdikadir Sharif Mohamed
• Mohamud Mohamed Gurhan
• Zamzam Abdulkadir Abdi
• Shaafi'e Abdillahi Hussein