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.
Dancer in the Dark: Synthesizing and Evaluating Polyglots for Blind Cross-Site Scripting.
Proc. of the 33rd USENIX Security Symposium, 2024. (to appear)
On the Detection of Image-Scaling Attacks in Machine Learning.
Proc. of the 39th Annual Computer Security Applications Conference (ACSAC), 2023.
PAVUDI: Patch-based Vulnerability Discovery using Machine Learning.
Proc. of the 39th Annual Computer Security Applications Conference (ACSAC), 2023.
Broken Promises: Measuring Confounding Effects in Learning-based Vulnerability Discovery.
Proc. of the 16th ACM Workshop on Artificial Intelligence and Security (AISEC), 2023.
See all publications of the research group.
AIGENCY — Opportunities and Risks of Generative AI in Security
The project aims to systematically investigate the opportunities and risks of generative artificial intelligence in computer security. It explores generative models as a new tool as well as a new threat. The project is joint work with Fraunhofer AISEC, CISPA, FU Berlin, and Aleph Alpha.
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.
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.
See all projects of the research group.
We are generally looking for motivated and skilled PhD students and postdocs to join our group—even if we currently do not announce open positions. If you are passionate about research and interested in combining machine learning and computer security, contact us directly at jobs@mlsec.org.
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Technische Universität Berlin
Machine Learning and Security, TEL 8-2
Ernst-Reuter-Platz 7
10587 Berlin, Germany
Office: office@mlsec.tu-berlin.de
Responsibility under the German Press Law §55 Sect. 2 RStV:
Prof. Dr. Konrad Rieck