KDD 99 Anomaly Detection Application

This is a Flask web application for predicting anomalies in the KDD 99 dataset using Decision Tree Classifier Model.

Features

• Allows users to input features from the KDD 99 dataset and predict whether an instance is an anomaly or not.
• Provides a user-friendly interface for interacting with the prediction model.
• Utilizes a machine learning model trained on the KDD 99 dataset to make predictions.
• Supports real-time prediction with low latency.

Installation

1. Clone the repository: /~https://github.com/amanovishnu/anamoly_detection.git

2. Install the required dependencies: pip install -r requirements.txt

Usage

1. Navigate to the project directory: cd anamoly_detection

2. Run the Flask application: python app.py

3. Access the application in your web browser at http://localhost:5000.

4. Input the required features from the KDD 99 dataset and submit the form to make predictions.

Deployment

The application can be deployed using various techniques such as traditional web hosting, containerization (Docker), serverless computing, Platform as a Service (PaaS), or Continuous Integration/Continuous Deployment (CI/CD) pipelines.

Contributing

Contributions are welcome! If you'd like to contribute to this project, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/new-feature).
3. Make your changes and commit them (git commit -am 'Add new feature').
4. Push to the branch (git push origin feature/new-feature).
5. Create a new pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

• This project utilizes the KDD 99 dataset for anomaly detection.
• Special thanks to contributors and open-source projects used in this application.