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.
IT-Administrator — Salary Grade TV-L E 12
We are looking for a full-time employee in information and communication technology to oversee the IT infrastructure of the center BIFOLD. The position primarily entails designing, configuring, and managing network and computing services to facilitate the center's research activities.
Foreign Language Secretary — Salary Grade TV-L E 7
We are seeking a foreign language secretary (Fremdsprachensekretär:in) to work with our chair and the Chair of Big Data Engineering. The main responsibilities of this position include secretarial and administrative support of research and teaching, as well as handling general correspondence.
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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.
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.
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No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning.
Proc. of the 32nd USENIX Security Symposium, 2023. (to appear)
Detecting Backdoors in Collaboration Graphs of Software Repositories.
Proc. of the 14th ACM Conference on Data and Applications Security and Privacy (CODASPY), 2023.
Machine Unlearning of Features and Labels.
Proc. of the 30th Network and Distributed System Security Symposium (NDSS), 2023.
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Technische Universität Berlin
Machine Learning and Security, TEL 8-2
10587 Berlin, Germany
Responsibility under the German Press Law §55 Sect. 2 RStV:
Prof. Dr. Konrad Rieck