This project is a Network Intrusion Detection System (NIDS) that uses machine learning algorithms to detect various types of network intrusions. The system is built using Flask for the web interface and scikit-learn for the machine learning models.
app.py: The main Flask application file.models/: Directory containing the trained machine learning model.NSL_Dataset/: Directory containing the training and testing datasets.static/: Directory containing static files like CSS.templates/: Directory containing HTML templates.corrm.csv: Correlation matrix CSV file.num_summary.csv: Numerical summary CSV file.pandas_profiling.html: HTML report generated by pandas profiling.requirements.txt: List of Python dependencies.
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Clone the repository:
git clone https://github.com/SudoAnirudh/Intrusion-Detection-System-Using-ML.git cd nids -
Create a virtual environment and activate it:
python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate`
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Install the required packages:
pip install -r requirements.txt
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Download the NSL-KDD dataset and place
Train.txtandTest.txtin the directory.
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Run the Flask application:
python app.py
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Open your web browser and go to
http://127.0.0.1:5000/. -
Use the web interface to input network features and get predictions for potential intrusions.
- Machine Learning Models: Uses various machine learning algorithms like Decision Trees, Random Forest, SVM, etc., to detect intrusions.
- Web Interface: User-friendly web interface to input network features and view predictions.
- Email Alerts: Sends email alerts when an intrusion is detected.
Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.
This project is licensed under the MIT License. See the LICENSE file for details.
Thanks to vicky60629 for the base of this project.