Machine Learning for Computer Security


SemesterSommer 2022
Course typeLecture
LecturerJun.-Prof. Dr. Wressnegger
AudienceInformatik Master & Bachelor
Credits3 ECTS
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.


25. AprilIntroductionLIVE!
02. MayMachine Learning 101, ,
09. MayFrom Data to Features, ,
16. MayEfficient String Processing , , ,
23. MayAnomaly Detection for Intrusion Detection , , ,
30. JuneMalware Classification , , ,
06. JuneNo lecture
13. JuneEvaluating Learning-based Systems
(Guest Lecture by Dr. Daniel Arp, TU Berlin)
20. JuneLearning Vulnerable Code Patterns , , ,
27. JuneLearning-based Fuzzing , , ,
04. JulyExplainable Machine Learning , , ,
11. JulyAdversarial Machine Learning , , ,
18. JulySummary and OutlookLIVE!
08. August (14:00-15:30)Written Exam (Building 20.40 HS17 + HS37)

Mailing List

News about the lecture, potential updates of the schedule, and additional material are distributed using a separate mailing list. Moreover, the list enables students to discuss topics of the lecture.

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