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Empirical & Behavioral Security

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Empirical & Behavioral Security

Ultimately, we need solutions that work both in theory and practice. However, as truth is stranger than fiction, this means that to attain this grand goal we cannot simply retreat to our study but will need to draw insight from empirical data. That is, unless a tool is easy to use properly, although it may be secure in theory it can still prove a liability in practice -- which goes for both end-users and developers. Moreover, we cannot preempt every possible attack, and hence need techniques that can detect and name subtle patterns in data that we can act upon. In short, this research area aims to devise an engineering process that significantly improves the security and privacy of today's real-world software, that keep pace with the continuing growth in complexity for future IT systems, and that is conveniently usable even by layman users and developers. It provides empirical methods and tools for dealing with unstructured, heterogeneous datasets at scale, techniques for ensuring the security of web applications and services; as well as usable and effective solutions for application development and maintenance.

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Most Recent Publications

Title Date Authors Meta
 2020
2020
 Sebastian Roth, Michael Backes, Ben Stock
 NRA5
 AsiaCCS 2020
 2020
NDSS
2020
 Sebastian Roth, Timothy Barron, Stefano Calzavara, Nick Nikiforakis, Ben Stock
 NRA5 , NRA3
 NDSS 2020
 2019
CCS
2019
 Christian Tiefenau, Emanuel von Zezschwitz, Maximilian Häring, Katharina Krombholz, Matthew Smith
 NRA5
 CCS 19
 2019
AAAI
2019
 Alexander Marx, Jilles Vreeken
 NRA5
 Proceedings of the First AAAI Spring Symposium Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI
 2019
ICCV
2019
 Ning Yu, Larry Davis, Mario Fritz
 NRA1 , NRA5
 International Conference on Computer Vision (ICCV)