Security Analysis of Forward Secure Log Sealing in Journald
Dörre, F.; Ottenhues, A.
2025. International Conference on Applied Cryptography and Network Security, 27 S
Research at KASTEL SRL
Research at KASTEL Security Research Labs is distinguished by its broad scope. Privacy and reliability aspects are naturally part of IT security; basic and applied research complement each other, and a comprehensive approach to IT security needs to look not just at the technical system, but also at its social environment as well as its human aspects.
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Fact 1
414 Publications from 2021 to 2024
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85 Papers at 33 Top Conferences 2021 to 2024
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36 Awards from 2021 to 2024
Our research covers a broad spectrum of cybersecurity. It combines the view of different disciplines in order to adress not only technical aspects, but also human factors and the legal framework.
Research Projects
Security for the Internet of Medical Things
Together with Wibu-Systems AG, KASTEL Security Research Labs is researching the project “Security for the Internet of Medical Things”, or “Sec4IoMT” for short. The aim is to find and exemplarily implement a solution approach for the long-term preservation of the security of Internet of Medical Things (IoMT) devices in critical infrastructures. When updating the security of individual components, the interoperability of the devices should be maintained.. At the same time, a security update must not lead to a loss of certification for the medical devices.
Research Projects
Secure Federated Machine Learning
Machine learning models require large amounts of data to be trained on, leaving little training data available in use cases with strong privacy requirements.The Secure Federated Machine Learning project of SAP and KASTEL Security Research Labs is investigating approaches for training a neural network on data from multiple parties without having to reveal their data to the other parties. These approaches can then be used in use cases with strong privacy requirements.
KASTEL Security Research Labs conduct interdisciplinary and application-oriented research. Therefore, we permanently maintain and expand numerous partnerships with industrial enterprises and research institutions. The resulting synergy effects flow into our research.
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119 Academic Cooperations in 19 Countries
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20 New Projects in 2023 and 2024
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88 Cooperations with Companies
Cooperations and Exchanges
Propolis Privacy for Smart Cities
2021, the German-French cooperation PROPOLIS was launched. Prof. Thorsten Strufe from KASTEL Security Research Labs at KIT, together with SAP, the Urban Software Institute GmbH, Chemnitz, and EURECOM Frankreich, is analyzing AI based data protection for smart cities and citizens. The project will be concluded in 2024. Read more about PROPOLIS
Cooperations and Exchanges
SofDCar – Software-Defined Car
Since August 2021, a total of 13 companies and research institutions have been working in the publicly funded “Software-Defined Car” project (SofDCar for short). The experts from industry and science want to create standardized rules and processes together for the car of the future and its effective use of data. The project is publicly funded by the German Federal Ministry of Economic Affairs and Climate Action (BMWK). Read more about SofDCar
Publications
Analytical Evaluation of Time-based Cryptography
Ramadan, M.; Gadekar, P.; Hagenmeyer, V.; Elbez, G.
2025. Title: Proceedings of the 11th International Conference on Information Systems Security and Privacy 2025. Editor: Gabriele Lenzini, Paolo Mori, Steven Furnell (Eds.)
Do Large Language Models Contain Software Architectural Knowledge? An Exploratory Case Study with GPT
Soliman, M.; Keim, J.
2025. 22nd IEEE International Conference on Software Architecture (ICSA 2025), Institute of Electrical and Electronics Engineers (IEEE)
SoK: The past decade of user deception in emails and today’s email clients’ susceptibility to phishing techniques
Veit, M. F.; Wiese, O.; Ballreich, F. L.; Volkamer, M.; Engels, D.; Mayer, P.
2025. Computers & Security, 150, 104197.
Two Sides of the Same Coin: Learning the Backdoor to Remove the Backdoor
Zhao, Q.; Wressnegger, C.
2025. Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence (AAAI)
DUMPLING: Fine-grained Differential JavaScript Engine Fuzzing
Wachter, L.; Gremminger, J.; Wressnegger, C.; Payer, M.; Toffalini, F.
2025. Proceedings of the Network and Distributed System Security Symposium (NDSS)