Machine Learning
and Security
Website of the
Chair of Machine Learning and Security
View from our building over Berlin.

Welcome and Overview

Our research group conducts fundamental research at the intersection of computer security and machine learning. On the one end, we are interested in developing intelligent systems that can learn to protect computers from attacks and identify security problems automatically. On the other end, we explore the security and privacy of machine learning by developing novel attacks and defenses.

We are part of the new Berlin Institute for the Foundations of Learning and Data (BIFOLD). Previously, we have been working at Technische Universität Braunschweig and the University of Göttingen.

We are hiring!

IT-Administrator — Salary Grade TV-L E 12

We are looking for a full-time employee in information and communication technology to oversee the IT infrastructure of the center BIFOLD. The position primarily entails designing, configuring, and managing network and computing services to facilitate the center's research activities.

Details (en) Details (de) Nr. IV-46/23      Deadline: April 6, 2023

Foreign Language Secretary — Salary Grade TV-L E 7

We are seeking a foreign language secretary (Fremdsprachensekretär:in) to work with our chair and the Chair of Big Data Engineering. The main responsibilities of this position include secretarial and administrative support of research and teaching, as well as handling general correspondence.

Details (en) Details (de) Nr. IV-18/23      Deadline: March 31, 2023

See all open positions.

Teaching in Summer

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.

Type: Integrated Lecture Audience: Master Module: #41101

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.

Type: Practical course Audience: Bachelor Module: #41100

See all teaching course.

Recent publications

No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning.
Thorsten Eisenhofer, Erwin Quiring, Jonas Möller, Doreen Riepel, Thorsten Holz and Konrad Rieck.
Proc. of the 32nd USENIX Security Symposium, 2023. (to appear)

PDF Code

Detecting Backdoors in Collaboration Graphs of Software Repositories.
Tom Ganz, Inaam Ashraf, Martin Härterich and Konrad Rieck.
Proc. of the 14th ACM Conference on Data and Applications Security and Privacy (CODASPY), 2023.


Machine Unlearning of Features and Labels.
Alexander Warnecke, Lukas Pirch, Christian Wressnegger and Konrad Rieck.
Proc. of the 30th Network and Distributed System Security Symposium (NDSS), 2023.

PDF Code

See all publications.


Technische Universität Berlin
Machine Learning and Security, TEL 8-2
Ernst-Reuter-Platz 7
10587 Berlin, Germany

Responsibility under the German Press Law §55 Sect. 2 RStV:
Prof. Dr. Konrad Rieck