Liam Tirpitz

dblp:287/7547 · DBLP profile ↗
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4ranked-venue papers
1as first author
4since 2021 · last 2025
0000-0003-0049-8112ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 3 · 1 first-author · 3 since 2021Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author · 2 since 2021Computer networks · 1 · 1 since 2021
YearPublicationVenuePosition
2025 Dataspaces for Collaborative Research
abstract
3835
Soo-Yon Kim, Liam Tirpitz, Max Wagels, Benedikt T. Arnold, Christian Rennert, István Koren, Janik Rapp, Mario Moser, Wil M. P. van der Aalst, Bernhard Rumpe, Robert H. Schmitt, Jan Pennekamp, Sandra Geisler
IEEE Big Data2
2025 Cross-Organizational Data Stream Management using Solid Data Spaces
Liam Tirpitz, Sandra Geisler
IEEE Big Data1
2025 CoFacS - Simulating a Complete Factory to Study the Security of Interconnected Production
abstract
While the digitization of industrial factories provides tremendous improvements for the production of goods, it also renders such systems vulnerable to serious cyber-attacks. To research, test, and validate security measures protecting industrial networks against such cyber-attacks, the security community relies on testbeds to simulate industrial systems, as utilizing live systems endangers costly components or even human life. However, existing testbeds focus on individual parts of typically complex production lines in industrial factories. Consequently, the impact of cyber-attacks on industrial networks as well as the effectiveness of countermeasures cannot be evaluated in an end-to-end manner. To address this issue and facilitate research on novel security mechanisms, we present CoFacS, the first COmplete FACtory Simulation that replicates an entire production line and affords the integration of real-life industrial applications. To showcase that CoFacS accurately captures real-world behavior, we validate it against a physical model factory widely used in security research. We show that CoFacS has a maximum deviation of 0.11% to the physical reference, which enables us to study the impact of physical attacks or network-based cyber-attacks. Moreover, we highlight how CoFacS enables security research through two cases studies surrounding attack detection and the resilience of 5G-based industrial communication against jamming.
Stefan Lenz, David Schachtschneider, Simon Jonas, Liam Tirpitz, Sandra Geisler, Martin Henze
LCN4
2021 HTTP Extensions for the Management of Highly Dynamic Data Resources
Lars Christoph Gleim, Liam Tirpitz, Stefan Decker
ESWC2