Machine Learning for Networking - Lab Exercises

This repository contains a collection of Jupyter Notebook lab exercises developed for the "Machine Learning for Networking" course at Politecnico di Torino, as part of the Master's degree in Cybersecurity. The exercises aim to provide hands-on experience in applying machine learning techniques to various networking challenges, including cybersecurity-related scenarios.

📑 Table of Contents

• Course Overview
• Repository Structure
• Requirements
• Installation and Usage
  • 1. Clone the Repository
  • 2. Set Up a Virtual Environment (Optional)
  • 3. Install Dependencies
  • 4. Run the Jupyter Notebooks
• Covered Topics
• Contributing
• License
• Acknowledgments

Course Overview

The "Machine Learning for Networking" course focuses on the intersection of machine learning (ML) and networking, providing students with both theoretical knowledge and practical skills. The primary goal is to understand how ML techniques can be leveraged to solve complex networking problems, particularly in the cybersecurity domain.

✅ Key Learning Objectives:

• Develop proficiency in Python programming for ML applications.
• Understand and implement data preprocessing and feature extraction techniques.
• Apply supervised and unsupervised learning methods to networking datasets.
• Explore anomaly detection and traffic classification for network security.
• Analyze real-world internet measurements and network traffic data.

Repository Structure

Machine-Learning-For-Networking/
├── labs/
│   ├── lab1                               
│   ├── lab2                               
│   ├── lab3                               
│   ├── lab4                               
│   ├── lab5                               
│   ├── lab6
│   ├── lab7
│   ├── lab9
│   └── lab10
├── requirements.txt                      # Python package dependencies
├── .gitignore                            # Git ignore rules
├── LICENSE                               # License information
└── README.md                             # Project documentation

Requirements

To run the lab exercises, you will need:

• Python 3.7+
• Jupyter Notebook or JupyterLab
• The libraries listed in requirements.txt, including:
  • numpy
  • pandas
  • matplotlib
  • scikit-learn
  • seaborn
  • jupyter

Install dependencies with:

pip install -r requirements.txt

Installation and Usage

1. Clone the Repository

git clone https://github.com/sroman0/Machine-Learning-For-Networking.git
cd Machine-Learning-For-Networking

2. Set Up a Virtual Environment (Optional)

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Run the Jupyter Notebooks

jupyter notebook

Navigate to the labs/ directory and open the desired notebook.

Covered Topics

The lab exercises cover a variety of topics, including but not limited to:

• Data Preprocessing: Handling missing values, normalization, and feature selection.
• Supervised Learning: Techniques such as decision trees, support vector machines (SVM), and neural networks.
• Unsupervised Learning: K-means clustering, hierarchical clustering, and PCA.
• Anomaly Detection: Identifying unusual patterns in network data.
• Traffic Classification: Categorizing network traffic for improved security monitoring.
• Visualization: Using tools like matplotlib and seaborn to interpret data and model performance.

Contributing

Contributions are welcome! If you have suggestions or improvements, please open an issue or submit a pull request. Make sure to follow the existing code style and include clear commit messages.

License

This project is licensed under the GNU General Public License v3.0.
See the LICENSE file for details.

Acknowledgments

• Developed for the "Machine Learning for Networking" course at Politecnico di Torino - Master's degree in Cybersecurity.
• Special thanks to the course instructors for their guidance and to fellow classmates for their collaboration.
• Datasets used in these labs are either publicly available or provided by the course instructors.

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For any questions, feel free to open an issue or contact the repository maintainer.