Machine Learning for Computer Security


SemesterWinter 2023
Course typeLecture + Exercises (NEW THIS YEAR!)
LecturerJun.-Prof. Dr. Wressnegger
AudienceInformatik Master & Bachelor
Credits3+2 ECTS
TimeWed, 11:30–13:00 (Lecture); Thu, 17:30–19:00 (Exercises)
Room-101 (50.34)

Award Winning Lecture

The lecture "Machine Learning for Computer Security" has been awarded as the "Beste Wahlvorlesung" at the KIT-Department of Informatics in the summer semester 2021.


The lecture is about combining the fields of machine learning and computer security in practice. Many tasks in the computer 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. However, also systems based on machine learning can be attacked and need to be secured.

The module introduces students to theoretic and practical aspects of machine learning in computer security. We cover basics on features, feature engineering, and feature spaces in the security domain, discuss the application of clustering and anomaly detection for malware analysis and intrusion detection, as well as, the discovery of vulnerabilities using machine learning. Additionally, we discuss the interpretability and robustness of learning-based systems.

Mode of Operation

This year, we do a regular lecture with course contents presented in-person. Additionally, during exercise hours, we discuss solutions to the exercises tasked that week. Participation in the exercises is mandatory to complete the MLSEC module.


Wed, 08. NovIntroduction
Thu, 09. NovPython 101
Wed, 15. Nov (+ Thu, 16.Nov)Machine Learning 101
Wed, 22. Nov (+ Thu, 23.Nov)From Data to Features
Wed, 29. Nov (+ Thu, 30. Nov)No Lecture/ Exercises
Wed, 06. Dec (+ Thu, 07.Dec)Efficient String Processing
Wed, 13. Dec (+ Thu, 14.Dec)Anomaly Detection for Intrusion Detection
Wed, 20. Dec (+ Thu, 21.Dec)Evaluating Learning-based Systems
(Guest Lecture by Dr. Daniel Arp, TU Berlin)
Wed, 10. Jan (+ Thu, 11. Jan)Malware Classification
Wed, 17. Jan (+ Thu, 18. Jan)Learning Vulnerable Code Patterns
Wed, 24. Jan (+ Thu, 25. Jan)Learning-based Fuzzing
Wed, 31. Jan (+ Thu, 01. Feb)Explainable Machine Learning
Wed, 07. Feb (+ Thu, 08. Feb)Adversarial Machine Learning
Thu, 15. FebSummary and Outlook
21. FebWritten Exam 20.40 (13:00–15:00)
Fritz-Haller Hörsaal (HS37)

Matrix Chat

News about the lecture, 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.