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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
USENIX-Security
2020
 Stefano Calzavara, Sebastian Roth, Alvise Rabitti, Michael Backes, Ben Stock
 NRA5 , NRA3
 Proceedings of the 29th USENIX Security Symposium
 2020
2020
 Sebastian Roth, Michael Backes, Ben Stock
 NRA5
 AsiaCCS 2020
 2020
IEEE S&P
2020
 Qingchuan Zhao, Chaoshun Zuo, Dolan-Gavitt Brendan, Giancarlo Pellegrino, Zhiqiang Lin
 NRA5 , NRA3
 IEEE Symposium on Security and Privacy
 2020
IEEE S&P
2020
 Rakibul Hasan, David Crandall, Mario Fritz, Apu Kapadia
 NRA1 , NRA5
 IEEE Symposium on Security and Privacy (S&P)
 2020
NDSS
2020
 Sebastian Roth, Timothy Barron, Stefano Calzavara, Nick Nikiforakis, Ben Stock
 NRA5 , NRA3
 NDSS 2020