TY - CHAP A1 - Knierim, Pascal A1 - Prange, Sarah A1 - Alt, Florian A1 - Feger, Sebastian A1 - Schneegass, Stefan A1 - Sasse, M. Angela A1 - Bayerl, Dominik A1 - Hof, Hans-Joachim ED - Marky, Karola ED - Grünefeld, Uwe ED - Kosch, Thomas T1 - Inclusive Security by Design T2 - Mensch und Computer 2022 - Workshopband UR - https://doi.org/10.18420/muc2022-mci-ws14-128 KW - usable security KW - inclusion KW - self-determined security Y1 - 2022 UR - https://doi.org/10.18420/muc2022-mci-ws14-128 PB - Gesellschaft für Informatik e.V. CY - Bonn ER - TY - CHAP A1 - Bayerl, Dominik A1 - Hutzelmann, Thomas A1 - Hof, Hans-Joachim T1 - Efficient Cross-Architecture Binary Function Embeddings through Knowledge Distillation T2 - Proceedings of the 11th ACM Cyber-Physical System Security Workshop N2 - 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. UR - https://doi.org/10.1145/3709017.3737709 Y1 - 2025 UR - https://doi.org/10.1145/3709017.3737709 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-61584 SN - 979-8-4007-1413-9 SP - 43 EP - 51 PB - ACM CY - New York ER - TY - JOUR A1 - Löw, Jakob A1 - Bayerl, Dominik A1 - Hof, Hans-Joachim T1 - Implementation of a Software Based Glitching Detection Mechanism JF - International Journal on Advances in Security KW - computer security KW - clocks KW - microcontrollers KW - program compilers KW - program control structures Y1 - 2022 UR - https://www.iariajournals.org/security/tocv15n12.html SN - 1942-2636 VL - 15 IS - 1&2 SP - 31 EP - 40 PB - IARIA CY - [s. l.] ER - TY - JOUR A1 - Mayer, Kevin A1 - Bayerl, Dominik A1 - Hof, Hans-Joachim T1 - Überwachung in modernen Fahrzeugen BT - Ergebnisse einer fahrzeugforensischen Untersuchung JF - Datenschutz und Datensicherheit - DuD UR - https://doi.org/10.1007/s11623-021-1459-5 Y1 - 2021 UR - https://doi.org/10.1007/s11623-021-1459-5 SN - 1862-2607 VL - 45 IS - 6 SP - 399 EP - 403 PB - Springer CY - Berlin ER - TY - CHAP A1 - Löw, Jakob A1 - Bayerl, Dominik A1 - Mayer, Kevin A1 - Hof, Hans-Joachim T1 - DrainDead: Emptying Batteries of Parked Electric Vehicles T2 - Proceedings of the 3rd USENIX Symposium on Vehicle Security and Privacy (VehicleSec ’25) Y1 - 2025 UR - https://www.usenix.org/conference/vehiclesec25/presentation/low SN - 978-1-939133-49-6 SP - 233 EP - 241 PB - USENIX Association CY - Berkeley ER - TY - CHAP A1 - Löw, Jakob A1 - Bayerl, Dominik A1 - Hof, Hans-Joachim ED - Hof, Hans-Joachim ED - Popescu, Manuela ED - Fongen, Anders T1 - Software Based Glitching Detection T2 - SECURWARE 2021: The Fifteenth International Conference on Emerging Security Information, Systems and Technologies KW - computer security KW - clocks KW - microcontrollers KW - program compilers KW - program control structures Y1 - 2021 UR - https://www.thinkmind.org/index.php?view=article&articleid=securware_2021_1_70_30029 SN - 978-1-61208-919-5 SN - 2162-2116 SP - 41 EP - 46 PB - IARIA CY - [s. l.] ER - TY - CHAP A1 - Ullrich, Henning A1 - Bayerl, Dominik A1 - Hutzelmann, Thomas A1 - Hof, Hans-Joachim T1 - Application of Quantum Key Distribution in Intelligent Transport Systems T2 - Proceedings of the 2nd Cyber Security in CarS Workshop (CSCS’25) N2 - 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. UR - https://doi.org/10.1145/3736130.3762692 Y1 - 2025 UR - https://doi.org/10.1145/3736130.3762692 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63186 SN - 979-8-4007-1928-8 PB - ACM CY - New York ER -