Research Group
Machine Learning
and Security
View from our building over Berlin.


We offer a number of courses each semester that revolve around machine learning and security. These include lectures on learning algorithms in security systems and adversarial machine learning as well as our labs where people can experiment with attacks and malicious code. Teaching is fun for us and so we have been able to even win awards for our lectures and practical courses.

Summer 2024

MLSEC — Machine Learning for Computer Security

This integrated lecture is concerned with using machine learning in computer security. Many tasks in security, such as the analysis of malicious software or the discovery of vulnerabilities, rest on manual work. Methods from machine learning can help accelerate this process and make security systems more intelligent. The lecture explores different approaches for constructing such learning-based security systems.

   Course Website    Module 41101 Type: Lecture Audience: Master

SECLAB — Applied Security Lab

This lab is a hands-on, entry-level course that explores the security analysis of systems. It provides an introduction to practical system security and serves a preparation for later advanced security labs. This includes developing strategies and tools for security analysis as well as investigating the security of real-world systems. In each unit of the lab, a different system is analyzed, ranging from Android applications to network hosts.

   Course Website    Module 41100 Type: Lab course Audience: Bachelor, Master

SEGA — Security Playground for Generative Agents

This project deals with the development of a security playground for generative agents. The agents are small characters that autonomously move through the playground and talk with each other. They are controlled by large language models, such as ChatGPT. The user can instruct the agents and simulate different attacks with them. The students develop the playground and the agents in teams. The project is aimed at Master students. A good understanding of web technology and good programming skills are required.

   Course Website    Module 41102 Type: Project Audience: Master

STEMO — Steganography with Language Models

This project explores how large language modules, such as ChatGPT, can be used for steganography. Students will form a red team (attackers) and a blue team (defenders). The red team will develop techniques to hide secret messages in generated texts, while the blue team will develop methods to detect these messages. The color of the teams will change after some time. The project is aimed at Master students. A good understanding of language models and strong programming skills are required.

   Course Website    Module 41102 Type: Project Audience: Master

PASIL — Privacy and Security in Learning

This block seminar focuses on privacy and security in machine learning. We will examine recent attacks on learning algorithms and discuss their impact on practical privacy and security. We will also look at possible defenses and countermeasures to protect learning algorithms and the underlying data. The seminar is intended for Master students.

   Course Website    Module 41104 Type: Seminar Audience: Master

MOPS — Mobile Privacy and Security

This block seminar deals with the security and privacy of mobile devices. We will discuss different concepts for analyzing and detecting security threats, such as attacks and malicious software. Futhermore, we will explore defense strategies suitable for mobile environments. The seminar is intended for Bachelor students. A good understanding of computer security is required.

   Course Website    Module 41103 Type: Seminar Audience: Bachelor

All Courses

Below is a list of all the courses we have offered in recent years. Note that some courses are not offered regularly, while others are planned and not yet available. Please consult the respective pages on the ISIS platform of TU Berlin.

Thesis Topics

Are you looking for an exciting topic for your Bachelor or Master thesis? We offer research-oriented thesis topics on machine learning and security, which we design together with the students. Contact Prof. Rieck by email and ask for further details. Please include the result of (23**42)%2248 in the subject line.