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.

Recent publications

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

Dos and Don'ts of Machine Learning in Computer Security.
Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro and Konrad Rieck.
Proc. of the 31st USENIX Security Symposium, 2022.
Distinguished Paper Award

PDF Project

See all publications.

Running projects

MALFOY — Machine Learning for Offensive Computer Security

The ERC Consolidator Grant MALFOY explores the application of machine learning in offensive computer security. It is an effort to understand how learning algorithms can be used by attackers and how this threat can be effectively mitigated.

ERC 2023 – 2028 Website

ALISON — Attacks against Machine Learning in Structured Domains

The goal of this project is to investigate the security of learning algorithms in structured domains. That is, the project develops a better understanding of attacks and defenses that operate in the problem space of learning algorithms rather than the feature space.

DFG 2023 – 2026

TELLY — Testing the Limits of Machine Learning in Vulnerability Discovery

The project aims to open the black box of machine learning in vulnerability discovery. Its goal is to systematically assess the limits of learning-based discovery approaches and derive a better understanding of their role in security. The project is part of the excellence cluster CASA.

DFG 2023 – 2026

See all research projects.

Contact

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
Email: rieck@tu-berlin.de