https://github.com/clandolt/mlcysec_notebooks
Repository of Jupyter notebook tutorials for teaching the Machine Learning in Cybersecurity Course at the Saarland University, WiSe 2025/26
https://github.com/clandolt/mlcysec_notebooks
anomaly-detection cybersecurity deep-learning gan intrusion-detection machine-learning
Last synced: about 21 hours ago
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Repository of Jupyter notebook tutorials for teaching the Machine Learning in Cybersecurity Course at the Saarland University, WiSe 2025/26
- Host: GitHub
- URL: https://github.com/clandolt/mlcysec_notebooks
- Owner: clandolt
- License: mit
- Created: 2025-10-08T15:07:53.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2026-01-12T14:17:28.000Z (6 months ago)
- Last Synced: 2026-01-12T20:55:50.261Z (6 months ago)
- Topics: anomaly-detection, cybersecurity, deep-learning, gan, intrusion-detection, machine-learning
- Language: Jupyter Notebook
- Homepage: https://christophlandolt.com/mlcysec_notebooks/
- Size: 33.4 MB
- Stars: 8
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
CISPA Machine Learning in Cybersecurity
=======================================
Repository of Jupyter notebook tutorials for teaching the Machine Learning in Cybersecurity Course at the Saarland University, WiSe 2025/26
> **Note:** You can browse the rendered notebooks directly in your browser via the [course website](https://cms.cispa.saarland/mlcysec_ws25/).
---
## 📘 Course Information
**Course website:** [https://cms.cispa.saarland/mlcysec_ws25/](https://cms.cispa.saarland/mlcysec_ws25/)
**Course edition:** Winter term 2025/2026 (Oct 13 – Feb 06)
**Recordings:** Will follow
**Instructor:** Christoph R. Landolt
---
## ⚙️ How to Run the Notebooks
The website hosts HTML-exported versions of the notebooks for convenient reading on any device.
However, we encourage you to run them yourself to gain hands-on experience.
You can do this in three main ways:
### 🖥️ Run Locally (CPU)
All notebooks are available in this GitHub repository.
You can also find them here:
👉 [https://github.com/clandolt/mlcysec_notebooks](https://github.com/clandolt/mlcysec_notebooks)
- Designed to run on standard laptops (no GPU required).
### ☁️ Google Colab
Prefer to use a hosted environment or want GPU support? Use [Google Colab](https://colab.research.google.com/notebooks/intro.ipynb#recent=true).
- Each notebook includes a “Run in Colab” badge on the documentation website.
- Enable GPU support via: `Runtime → Change runtime type → GPU`.
---
## 🧭 Tutorial Lessons
The **Exercise Schedule** (below) lists the practical/tutorial sessions associated with the course tutorials.
| Date | Time | Topic |
|------|------|-------|
| **29.10.2025** | 16:15–17:45 | Tutorial: ML Basics / Setup |
| **05.11.2025** | 16:15–17:45 | Q&A: ML Basics |
| **12.11.2025** | 16:15–17:45 | Introduction Ex1: Train ML IDS |
| **03.12.2025 (online)** | 16:15–17:45 | Ex1 Review: Train ML IDS |
| **10.12.2025** | 16:15–17:45 | Introduction Ex2: Evade ML IDS |
| **07.01.2026** | 16:15–17:45 | Ex2 Review: Evade ML IDS |
| **14.01.2026** | 16:15–17:45 | Introduction Ex3: AI for CTF |
| **04.02.2026** | 16:15–17:45 | Ex3 Review: AI for CTF |
---
## 💬 Feedback, Questions, or Contributions
This is the first edition of the **Machine Learning in Cybersecurity** tutorials.
We appreciate all feedback — whether it’s a typo, a bug, or a suggestion for improvement.
If you discover a **mistake or issue in a notebook**, please [open a GitHub issue](../../issues) so we can track and resolve it publicly.
You can also reach out directly via email (`christoph dot landolt at cispa dot de`), or speak to us during a exercise session.
If you find the tutorials helpful, please cite this course as:
```bibtex
@misc{landolt2025_mlcysec,
title = {CISPA Machine Learning in Cybersecurity},
author = {Christoph R. Landolt and Mario Fritz},
year = {2025},
howpublished = {\url{https://christophlandolt.com/mlcysec_notebooks/}},
}