Machine Learning
and Security
Temporary website of the
Chair of Machine Learning and Security at TU Berlin

Overview

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

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

Recent publications

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

Quantifying the Risk of Wormhole Attacks on Bluetooth Contact Tracing.
Stefan Czybik, Daniel Arp and Konrad Rieck.
Proc. of the 13th ACM Conference on Data and Applications Security and Privacy (CODASPY), 264–275, 2022.

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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

FG Machine Learning and Security
Technische Universität Berlin
Ernst-Reuter-Platz 7
10587 Berlin

Responsibility under the German Press Law §55 Sect. 2 RStV:
Prof. Dr. Konrad Rieck
Email: rieck@mlsec.org
Phone: +49 151 512-65917