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.

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.

   Course Website    Module 41101 Type: Integrated 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: Practical course Audience: Bachelor

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

Hunting for Truth: Analyzing Explanation Methods in Learning-based Vulnerability Discovery.
Tom Ganz, Philipp Rall, Martin Härterich and Konrad Rieck.
Proc. of the 8th IEEE European Symposium on Security and Privacy (EuroS&P), 2023. (to appear)


CodeGraphSMOTE: Data Augmentation for Vulnerability Discovery.
Tom Ganz, Erik Imgrund, Martin Härterich and Konrad Rieck.
Proc. of the IFIP Conference on Data and Applications Security and Privacy (DBSEC), 2023. (to appear)

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