Institut für Verteilte Systeme

Machine Learning & Security

Ankündigung

Diese Vorlesung findet im Sommersemester 2025 das erste Mal statt.

 
Titel:Maschinelles Lernen & IT-Sicherheit
English title:Machine Learning & Security
Type:Lecture with lab
Kürzel / Nr.:MLS / CS6975.000
SWS / LP:2V+2Ü / 6LP
Lecturer:Prof. Dr. Frank Kargl
Lab:Dennis Eisermann
Dates:

Lecture: Tuesdays 8:30 - 10:00, O28 / 1002
Lab: Mondays 14:15 - 15:45, O27 / 2203

Start of lecture: Tuesday, 22.05.2025

eLearning Plattform:You can find the corresponding Moodle course. 
Grade bonus:See moodle.
Examination:Oral exams will be held on individual appointment after end of the lecture.

General Module Information

Assignment to study programs:
  • 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
Teaching Format:Lecture Machine Learning & Security (Prof. Dr. Frank Kargl)
Lab Machine Learning & Security (Dennis Eisermann)
Responsible Teacher:Prof. Dr. Frank Kargl
Language:Englisch
Semester / Duration:each summer term / one semester
Prerequisites (content):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.
Prerequisistes (formal):-
Basis for (content):Projects and M.Sc. theses in this area
Learning Goals:

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.
Content:

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.
Literature:
Assessment:The module examination consists of a graded written or oral examination, depending on the number of participants. If a specified academic work is achieved, a grade bonus is awarded in accordance with §17 (3a) of the General Examination Regulations at the immediately following examination. The examination grade is improved by one grade level, but not better than 1.0. An improvement from 5.0 to 4.0 is not possible. The examination form will be announced in good time before the examination is held - at least 4 weeks before the examination date.
Overall grade:Grade of the module exam
Effort:Presence teaching: 60 h
Self-study: 120 h
Total: 180 h