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.
Machine Unlearning of Features and Labels.
Proc. of the 30th Network and Distributed System Security Symposium (NDSS), 2023.
Dos and Don'ts of Machine Learning in Computer Security.
Proc. of the 31st USENIX Security Symposium, 2022.
Distinguished Paper Award
Quantifying the Risk of Wormhole Attacks on Bluetooth Contact Tracing.
Proc. of the 13th ACM Conference on Data and Applications Security and Privacy (CODASPY), 264–275, 2022.
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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.
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.
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