Display language
To modulepage Generate PDF

#41101 / #1

Seit SoSe 2023


Machine Learning for Computer Security


Rieck, Konrad


Schriftliche Prüfung


Fakultät IV

Institut für Softwaretechnik und Theoretische Informatik

34353000 FG Maschinelles Lernen und IT-Sicherheit

No information


No information

Rieck, Konrad


Learning Outcomes

Students have a comprehensive understanding of how machine learning is applied to security problems. They are able to design feature spaces for security data and select appropriate learning concepts and algorithms. They can develop learning-based systems for threat detection, malware analysis, and vulnerability discovery. They are also aware of security threats, such as poisoning and evasion, and know about countermeasures to mitigate them. They understand ethical implications of applying learning-based systems in practice.


Principles of machine learning for computer security; feature spaces and embeddings; threat and intrusion detection; malware analysis; vulnerability discovery; deanonymization attacks; poisoning and evasion of security systems; unsupervised and supervised learning concepts for security; algorithms for classification, anomaly detection, clustering, and dimension reduction on security data; experimental design principles

Module Components


All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Machine Learning for Computer SecurityIVSoSeEnglish4

Workload and Credit Points

Machine Learning for Computer Security (IV):

Workload descriptionMultiplierHoursTotal
Pre/post processing15.04.0h60.0h
Exercise tasks10.04.0h40.0h
Preparation exam1.020.0h20.0h
180.0h(~6 LP)
The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

This integrated course consists of a lecture combined with exercise assignments. The latter require independently solving programming exercises with the guidance of a teaching assistant.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

- Highly recommended: programming skills in Python; basic knowledge in security - Helpful, but not obligatory: Basic knowledge in machine learning.

Mandatory requirements for the module test application:

This module has no requirements.

Module completion



Type of exam

Written exam




90 minutes

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:

Maximum Number of Participants

This module is not limited to a number of students.

Registration Procedures

Registration is not required, but stating the interest to participate in the lecture is welcome for the planning of resources. This is done by registering for the course at the teaching platform ISIS.

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable


Electronical lecture notes

Availability:  unavailable



Recommended literature
No recommended literature given

Assigned Degree Programs

This module is used in the following Degree Programs (new System):

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Computer Engineering (M. Sc.)115SoSe 2023SoSe 2024
Computer Science (Informatik) (M. Sc.)118SoSe 2023SoSe 2024
Elektrotechnik (M. Sc.)19SoSe 2023SoSe 2024
Information Systems Management (Wirtschaftsinformatik) (M. Sc.)16SoSe 2023SoSe 2024


No information