Introduction to the course

Info about the course

Requirements

• Fundamentals of networking
• Basic/intermediate Python programming
• General understanding of network security

Course organisation

• Part theory and part laboratory
  • The laboratories will be executed using your personal computers
    • -> Minimal configuration on your PC is required
• Let’s look at the
  • syllabus (https://unitn.coursecatalogue.cineca.it/insegnamenti/ 2024/50174_642668_96291/2023/50178/10756?coorte=2023&schemaid=8343)
  • moodle (https://didatticaonline.unitn.it/dol/course/view.php?id=39648)
  • calendar (next slide)

Tools used during the course

Python 3.X

• Including scientific libraries
  • Scikit-learn (https://scikit-learn.org/)
  • NumPy (https://numpy.org/)
  • Keras and Tensorflow (https://keras.io/ and https://www.tensorflow.org/)
• Pyshark (https://pypi.org/project/pyshark/)
• Jupyter (https://jupyter.org/)
• IDE with Python language support
  • PyCharm (https://www.jetbrains.com/pycharm/)
  • Or Visual Studio Code (https://code.visualstudio.com/)

Useful links

• The source code of the laboratories will be publicly available (https:// github.com/doriguzzi/network-intrusion-and-anomaly-detection-withml-2024-2025)
• The repository will also provide the guidelines for setting up the development environment
• More material is available at:https://github.com/doriguzzi, such as:
  • LUCID, a DL-based solution for DDoS attack detection
  • FLAD, a Federated Learning approach for training intrusion detection systems