Hot Topics in Machine Learning for Computer Security


SemesterSummer 2024
Course typeBlock Seminar
LecturerJun.-Prof. Dr. Wressnegger
AudienceInformatik Master & Bachelor
Credits4 ECTS
Room148 (50.34)


This seminar is concerned with the combination of machine learning and computer security in practice. Many tasks in the security landscape are based on manual labor, such as searching for vulnerabilities or analyzing malware. Here, machine learning can be used to establish a higher degree of automation, providing more "intelligent" security solutions.

The module intensifies the contents of the MLSEC lectures, putting focus on timely topics from recent research. It teaches students to work up results from state-of-the-art research. To this end, the they will read up on a sub-field, prepare a seminar report, and present their work at the end of the term to their colleagues.


Tue, 16. April, 9:45–11:15Kick-off \& Topic presentation
Thu, 18. April, 11:59 (noon)Send topic selection
(assignment happens till 15:00)
Fri, 19. April, 11:59 (noon)Officially register for assigned topic
(missed opportunities will be reassigned to waiting list till 15:00)
Tue, 23. April, 9:45–11:15Optional unit on "How to Ace the Seminar"
Thu, 25. AprilArrange appointments with assistant
Mon, 29. April - Fri, 03. May1st individual meeting (Provide first overview and ToC)
Mon, 10. June - Fri, 14. June2nd individual meeting (Feedback on draft report)
Wed, 26. JuneSubmit final paper
Mon, 08. JulySubmit review for fellow students
Fri, 12. JulyEnd of discussion phase
Fri, 19. JulySubmit camera-ready version of your paper
Fri, 26. JulyPresentation at final colloquium

Matrix Chat

News about the seminar, potential updates to the schedule, and additional material are distributed using the course's matrix room. Moreover, matrix enables students to discuss topics and solution approaches.

You find the link to the matrix room on ILIAS.


Every student may choose one of the following topics. For each of these, we additionally provide recent top-tier publications that serve as the basis for the seminar report. For the seminar and your final report, you should not merely summarize these papers, but try to go beyond and arrive at your own conclusions.

  • Detecting DeepFake Videos

  • Learning-based Side-Channel Attacks

  • Explainable AI for Learning-Based Side-Channel Analysis

  • Poisoning Learning-based Code Assistants

  • Security Implications of Large Language Model Code Assistants

  • Security Testing with Large Language Models

  • Learning-based Attack Detection in Cyber-Physical Systems

  • Attacks Against the Software Supply-Chain