@inproceedings{KnierimPrangeAltetal.2022, author = {Knierim, Pascal and Prange, Sarah and Alt, Florian and Feger, Sebastian and Schneegass, Stefan and Sasse, M. Angela and Bayerl, Dominik and Hof, Hans-Joachim}, title = {Inclusive Security by Design}, booktitle = {Mensch und Computer 2022 - Workshopband}, editor = {Marky, Karola and Gr{\"u}nefeld, Uwe and Kosch, Thomas}, publisher = {Gesellschaft f{\"u}r Informatik e.V.}, address = {Bonn}, doi = {https://doi.org/10.18420/muc2022-mci-ws14-128}, year = {2022}, language = {en} } @inproceedings{BayerlHutzelmannHof2025, author = {Bayerl, Dominik and Hutzelmann, Thomas and Hof, Hans-Joachim}, title = {Efficient Cross-Architecture Binary Function Embeddings through Knowledge Distillation}, booktitle = {Proceedings of the 11th ACM Cyber-Physical System Security Workshop}, publisher = {ACM}, address = {New York}, isbn = {979-8-4007-1413-9}, doi = {https://doi.org/10.1145/3709017.3737709}, pages = {43 -- 51}, year = {2025}, abstract = {Deep learning has recently been shown to be effective in various tasks related to static binary analysis. One important analysis task is the binary function similarity problem: Given the binary code of two functions compiled with different compilers, different settings, and different processor architectures, the goal is to decide whether the functions are semantically equivalent (i.e. "similar") or not. This problem has numerous applications for embedded systems, for example plagiarism detection, validation of compliance restrictions with usable software licenses, more efficient reverse engineering of existing binary codebases, or vulnerability scanning by detecting known vulnerable functions. In this paper, we propose a novel training scheme for the popular transformer neural network architecture to learn function embeddings directly from instruction listings. Unlike existing approaches, our solution explicitly considers the cross-architecture scenario: we propose a training method to adapt the model to different instruction set architectures (ISA) without having to train a new model from scratch, which allows the model to also be used efficiently for embedded systems, where there are a variety of different processor architectures. We show that our solution achieves a similarity classification accuracy of 89.6\% on a dataset consisting of several real-world open source software projects. Finally, we conduct extensive experiments to demonstrate the effectiveness of knowledge distillation in increasing the computational efficiency of the embedding model. We demonstrate a reduction in the number of parameters from 87M to 23M, while still maintaining a classification accuracy of 87.8\%. Our code and artifacts are available as open source.}, language = {en} } @article{LoewBayerlHof2022, author = {L{\"o}w, Jakob and Bayerl, Dominik and Hof, Hans-Joachim}, title = {Implementation of a Software Based Glitching Detection Mechanism}, volume = {15}, journal = {International Journal on Advances in Security}, number = {1\&2}, publisher = {IARIA}, address = {[s. l.]}, issn = {1942-2636}, url = {https://www.iariajournals.org/security/tocv15n12.html}, pages = {31 -- 40}, year = {2022}, language = {en} } @article{MayerBayerlHof2021, author = {Mayer, Kevin and Bayerl, Dominik and Hof, Hans-Joachim}, title = {{\"U}berwachung in modernen Fahrzeugen}, volume = {45}, journal = {Datenschutz und Datensicherheit - DuD}, subtitle = {Ergebnisse einer fahrzeugforensischen Untersuchung}, number = {6}, publisher = {Springer}, address = {Berlin}, issn = {1862-2607}, doi = {https://doi.org/10.1007/s11623-021-1459-5}, pages = {399 -- 403}, year = {2021}, language = {de} } @inproceedings{LoewBayerlMayeretal.2025, author = {L{\"o}w, Jakob and Bayerl, Dominik and Mayer, Kevin and Hof, Hans-Joachim}, title = {DrainDead: Emptying Batteries of Parked Electric Vehicles}, booktitle = {Proceedings of the 3rd USENIX Symposium on Vehicle Security and Privacy (VehicleSec '25)}, publisher = {USENIX Association}, address = {Berkeley}, isbn = {978-1-939133-49-6}, url = {https://www.usenix.org/conference/vehiclesec25/presentation/low}, pages = {233 -- 241}, year = {2025}, language = {en} } @inproceedings{LoewBayerlHof2021, author = {L{\"o}w, Jakob and Bayerl, Dominik and Hof, Hans-Joachim}, title = {Software Based Glitching Detection}, booktitle = {SECURWARE 2021: The Fifteenth International Conference on Emerging Security Information, Systems and Technologies}, editor = {Hof, Hans-Joachim and Popescu, Manuela and Fongen, Anders}, publisher = {IARIA}, address = {[s. l.]}, isbn = {978-1-61208-919-5}, issn = {2162-2116}, url = {https://www.thinkmind.org/index.php?view=article\&articleid=securware_2021_1_70_30029}, pages = {41 -- 46}, year = {2021}, language = {en} } @inproceedings{UllrichBayerlHutzelmannetal.2025, author = {Ullrich, Henning and Bayerl, Dominik and Hutzelmann, Thomas and Hof, Hans-Joachim}, title = {Application of Quantum Key Distribution in Intelligent Transport Systems}, pages = {5}, booktitle = {Proceedings of the 2nd Cyber Security in CarS Workshop (CSCS'25)}, publisher = {ACM}, address = {New York}, isbn = {979-8-4007-1928-8}, doi = {https://doi.org/10.1145/3736130.3762692}, year = {2025}, abstract = {Quantum Key Distribution (QKD) enables two parties to generate a secret key, based on quantum physical properties. The secrecy of the exchanged key is guaranteed by these quantum physical properties, even against an attacker using a quantum computer. QKD therefore offers an Quantum Information Theoretically Secure (Q-ITS) alternative to Post Quantum Cryptography (PQC) schemes. This paper discusses advantages and challenges of deploying a QKD system in an Intelligent Transport System (ITS), by examining two exemplary use cases. Approaches to effectively implement QKD in these use cases are proposed, with a short discussion of advantages over the use of classical and PQC schemes.}, language = {en} }