| Einordnung in die Studiengänge: | * Informatik, B.Sc., FSPO 2021/Schwerpunkt Informatik
* Informatik, B.Sc., FSPO 2022/Vertiefungsbereich
* Informatik, M.Sc., FSPO 2021/Kernfach/Praktische und Angewandte Informatik
* Informatik, M.Sc., FSPO 2022/Kernbereich Informatik/Praktische Informatik
* Künstliche Intelligenz, M.Sc., FSPO 2021/Kernfach Künstliche Intelligenz/Praktische und Angewandte Informatik
* Künstliche Intelligenz, M.Sc., FSPO 2022/Kernbereich Künstliche Intelligenz/Praktische Informatik
* Medieninformatik, B.Sc., FSPO 2022/Vertiefungsbereich
* Medieninformatik, M.Sc., FSPO 2021/Kernfach/Praktische und Angewandte Informatik
* Medieninformatik, M.Sc., FSPO 2022/Kernbereich Medieninformatik/Praktische Informatik
* Software Engineering, B.Sc., FSPO 2022/Vertiefungsbereich/SE Wahlbereich
* Software Engineering, M.Sc., FSPO 2021/Kernfach/Praktische und Angewandte Informatik
* Software Engineering, M.Sc., FSPO 2022/Kernbereich Software Engineering/Praktische Informatik |
| Lehr- und Lernformen: | Lecture Machine Learning & Security (Prof. Dr. Frank Kargl)
Lab Machine Learning & Security (Dennis Eisermann) |
| Verantwortlich: | Prof. Dr. Frank Kargl |
| Dozent: | Prof. Dr. Frank Kargl |
| Unterrichtssprache: | Englisch |
| Turnus / Dauer: | each summer term / one semester |
| Voraussetzungen (inhaltlich): | Künstliche Intelligenz und Neuroinformatik (CS6395.000), Security in IT-Systems (CS6935.000)
Foundational knowledge on these topics is mandatory for this course! We strongly discourage from trying participation without this or equivalent knowledge. |
| Voraussetzungen (formal): | - |
| Grundlage für (inhaltlich): | Projects and M.Sc. theses in this area |
| Lernergebnisse: | Upon completing this module, students will
* understand existing threats to machine-learning as well as possible countermeasures,
* understand the application of machine-learning in security and in particular network security for tasks like security monitoring and intrusion detection,
* be able to implement robust and secure machine-learning systems,
* have developed practical skills in using ML-based tools for solving real-world problems in (network-)security,
* be able to implement and evaluate ML models for tasks such as anomaly detection, and malware identification. |
| Inhalt: | The module provides an in-depth exploration of the intersection of ML, and (network-)security, focusing on:
* Security of ML: Threats, risks, attack classes and mitigations.
* Application of ML in IT-Security: Using ML to detect and mitigate cyber threats for tasks like intrusion detection, malware analysis, or phishing defense.
* Case Studies: As part of the lab, students will be tasked with real-world scenarios from areas like security monitoring, anomaly, or phishing detection and challenged to innovate and enhance over existing solutions. |
| Literatur: | * Lecture notes, research papers, and case study materials will be provided as part of lecture material.
* Supplementing reading material: Clarence Chio, David Freeman, ‘Machine Learning and Security’, O’Reilly Media, Inc., ISBN: 9781491979907, https://learning.oreilly.com/library/view/machine-learning-and/9781491979891/ (available as ebook in KIZ library) |
| Bewertungsmethode: | Oral exams will be held on individual appointment after end of the lecture. |
| Arbeitsaufwand: | Presence teaching: 60 h
Self-study: 120 h
Total: 180 h |
