@article{SchueleSchliskiHutzelmannetal.2017, author = {Sch{\"u}le, Maximilian E. and Schliski, Pascal M. N. and Hutzelmann, Thomas and Rosenberger, Tobias and Leis, Viktor and Vorona, Dimitri and Kemper, Alfons and Neumann, Thomas}, title = {Monopedia: Staying Single is Good Enough - The HyPer Way for Web Scale Applications}, volume = {10}, journal = {Proceedings of the VLDB Endowment}, number = {12}, publisher = {ACM}, address = {New York}, issn = {2150-8097}, doi = {https://doi.org/10.14778/3137765.3137809}, pages = {1921 -- 1924}, year = {2017}, 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{HutzelmannMaukschPetrovskaetal.2023, author = {Hutzelmann, Thomas and Mauksch, Dominik and Petrovska, Ana and Pretschner, Alexander}, title = {Generation of Tailored and Confined Datasets for IDS Evaluation in Cyber-Physical Systems}, volume = {21}, journal = {IEEE Transactions on Dependable and Secure Computing}, number = {4}, publisher = {IEEE}, address = {New York}, issn = {1941-0018}, doi = {https://doi.rog/10.1109/TDSC.2023.3341211}, pages = {3948 -- 3962}, year = {2023}, abstract = {The state-of-the-art evaluation of an Intrusion Detection System (IDS) relies on benchmark datasets composed of the regular system's and potential attackers' behavior. The datasets are collected once and independently of the IDS under analysis. This paper questions this practice by introducing a methodology to elicit particularly challenging samples to benchmark a given IDS. In detail, we propose (1) six fitness functions quantifying the suitability of individual samples, particularly tailored for safety-critical cyber-physical systems, (2) a scenario-based methodology for attacks on networks to systematically deduce optimal samples in addition to previous datasets, and (3) a respective extension of the standard IDS evaluation methodology. We applied our methodology to two network-based IDSs defending an advanced driver assistance system. Our results indicate that different IDSs show strongly differing characteristics in their edge case classifications and that the original datasets used for evaluation do not include such challenging behavior. In the worst case, this causes a critical undetected attack, as we document for one IDS. Our findings highlight the need to tailor benchmark datasets to the individual IDS in a final evaluation step. Especially the manual investigation of selected samples from edge case classifications by domain experts is vital for assessing the IDSs.}, language = {en} } @article{PetrovskaKugeleHutzelmannetal.2022, author = {Petrovska, Ana and Kugele, Stefan and Hutzelmann, Thomas and Beffart, Theo and Bergemann, Sebastian and Pretschner, Alexander}, title = {Defining adaptivity and logical architecture for engineering (smart) self-adaptive cyber-physical systems}, volume = {2022}, pages = {106866}, journal = {Information and Software Technology}, number = {147}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0950-5849}, doi = {https://doi.org/10.1016/j.infsof.2022.106866}, year = {2022}, language = {en} } @inproceedings{PetrovskaHutzelmannKugele2023, author = {Petrovska, Ana and Hutzelmann, Thomas and Kugele, Stefan}, title = {A Theoretical Framework for Self-Adaptive Systems: Specifications, Formalisation, and Architectural Implications}, booktitle = {SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing}, publisher = {ACM}, address = {New York}, isbn = {978-1-4503-9517-5}, doi = {https://doi.org/10.1145/3555776.3577665}, pages = {1440 -- 1449}, year = {2023}, language = {en} } @inproceedings{ZieglmeierKaciankaHutzelmannetal.2019, author = {Zieglmeier, Valentin and Kacianka, Severin and Hutzelmann, Thomas and Pretschner, Alexander}, title = {A Real-Time Remote IDS Testbed for Connected Vehicles}, booktitle = {Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing}, publisher = {ACM}, address = {New York}, isbn = {978-1-4503-5933-7}, doi = {https://doi.org/10.1145/3297280.3297465}, pages = {1898 -- 1905}, year = {2019}, language = {en} } @inproceedings{LippElsnerHutzelmannetal.2022, author = {Lipp, Stephan and Elsner, Daniel and Hutzelmann, Thomas and Banescu, Sebastian and Pretschner, Alexander and B{\"o}hme, Marcel}, title = {FuzzTastic: A Fine-grained, Fuzzer-agnostic Coverage Analyzer}, booktitle = {Proceedings, 2022 ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings}, publisher = {ACM}, address = {New York}, issn = {978-1-4503-9223-5}, doi = {https://doi.org/10.1145/3510454.3516847}, pages = {75 -- 79}, year = {2022}, abstract = {Performing sound and fair fuzzer evaluations can be challenging, not only because of the randomness involved in fuzzing, but also due to the large number of fuzz tests generated. Existing evaluations use code coverage as a proxy measure for fuzzing effectiveness. Yet, instead of considering coverage of all generated fuzz inputs, they only consider the inputs stored in the fuzzer queue. However, as we show in this paper, this approach can lead to biased assessments due to path collisions. Therefore, we developed FuzzTastic, a fuzzeragnostic coverage analyzer that allows practitioners and researchers to perform uniform fuzzer evaluations that are not affected by such collisions. In addition, its time-stamped coverage-probing approach enables frequency-based coverage analysis to identify barely tested source code and to visualize fuzzing progress over time and across code. To foster further studies in this field, we make FuzzTastic, together with a benchmark dataset worth ~12 CPU-years of fuzzing, publicly available; the demo video can be found at https://youtu.be/Lm-eBx0aePA.}, language = {en} } @article{HutzelmannBanescuPretschner2019, author = {Hutzelmann, Thomas and Banescu, Sebastian and Pretschner, Alexander}, title = {A Comprehensive Attack and Defense Model for the Automotive Domain}, volume = {2}, pages = {11-02-01-0001}, journal = {SAE International Journal of Transportation Cybersecurity and Privacy}, number = {1}, publisher = {SAE International}, address = {Warrendale}, issn = {2572-1054}, doi = {https://doi.org/10.4271/11-02-01-0001}, year = {2019}, language = {en} } @unpublished{OgnawalaPretschnerHutzelmannetal.2018, author = {Ognawala, Saahil and Pretschner, Alexander and Hutzelmann, Thomas and Psallida, Eirini and Amato, Ricardo Nales}, title = {Reviewing KLEE's Sonar-Search Strategy in Context of Greybox Fuzzing}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.1803.04881}, year = {2018}, abstract = {Automatic test-case generation techniques of symbolic execution and fuzzing are the most widely used methods to discover vulnerabilities in, both, academia and industry. However, both these methods suffer from fundamental drawbacks that stop them from achieving high path coverage that may, consequently, lead to discovering vulnerabilities at the numerical scale of static analysis. In this presentation, we examine systems-under-test (SUTs) at the granularity level of functions and postulate that achieving higher function coverage (execution of functions in a program at least once) than, both, symbolic execution and fuzzing may be a necessary condition for discovering more vulnerabilities than both. We will start this presentation with the design of a targeted search strategy for KLEE, sonar-search, that prioritizes paths leading to a target function, rather than maximizing overall path coverage in the program. Then, we will show that examining SUTs at the level of functions (compositional analysis) leads to discovering more vulnerabilities than symbolic execution from a single entry point. Using this finding, we will, then, demonstrate a greybox fuzzing method that can achieve higher function coverage than symbolic execution. Finally, we will present a framework to effectively manage vulnerabilities and assess their severities.}, language = {en} } @inproceedings{OgnawalaHutzelmannPsallidaetal.2018, author = {Ognawala, Saahil and Hutzelmann, Thomas and Psallida, Eirini and Pretschner, Alexander}, title = {Improving Function Coverage with Munch: A Hybrid Fuzzing and Directed Symbolic Execution Approach}, booktitle = {Proceedings of the 33rd Annual ACM Symposium on Applied Computing}, publisher = {ACM}, address = {New York}, isbn = {978-1-4503-5191-1}, doi = {https://doi.org/10.1145/3167132.3167289}, pages = {1475 -- 1482}, year = {2018}, language = {en} } @inproceedings{HutzelmannMaukschPretschner2020, author = {Hutzelmann, Thomas and Mauksch, Dominik and Pretschner, Alexander}, title = {How to Conduct Experiments with a Real Car? Experiences and Practical Guidelines}, booktitle = {Software Architecture: 14th European Conference, ECSA 2020, Tracks and Workshops, L'Aquila, Italy, September 14-18, 2020, Proceedings}, editor = {Muccini, Henry and Avgeriou, Paris and Buhnova, Barbora and Camara, Javier and Caporuscio, Mauro and Franzago, Mirco and Koziolek, Anne and Scandurra, Patrizia and Trubiani, Catia and Weyns, Danny and Zdun, Uwe}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-59155-7}, doi = {https://doi.org/10.1007/978-3-030-59155-7_37}, pages = {518 -- 526}, year = {2020}, language = {en} } @phdthesis{Hutzelmann2024, author = {Hutzelmann, Thomas}, title = {A Holistic Methodology for Quality Assessment of Intrusion Detection Systems}, publisher = {Technische Universit{\"a}t M{\"u}nchen}, address = {M{\"u}nchen}, pages = {B, xii, 165}, school = {Technische Universit{\"a}t M{\"u}nchen}, year = {2024}, 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} } @inproceedings{LavesHutzelmannMayeretal.2025, author = {Laves, Claudius and Hutzelmann, Thomas and Mayer, Kevin and Hof, Hans-Joachim}, title = {Driving Down Premiums: A Security and Forensic Analysis of the DriveWell Insurance System}, pages = {9}, 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/10.1145/3736130.3762688}, year = {2025}, abstract = {Modern car insurance companies can dynamically adjust premiums with mobile sensor units that constantly record, analyze, and grade driving behavior. However, attackers can tamper with these systems to cheat the insurance company and reduce their premiums. We developed an attacker model for this threat in conjunction with a risk assessment using the ISO/SAE 21434 Risk Rating Methodology, a widely accepted standard for evaluating and managing risks in automotive systems. To support and showcase our model, we examined the static IT security of the mobile sensor unit DriveWell and its companion Android application HUK Mein Auto. In particular, we utilized digital forensic tools to analyze the stored data in the sensor's internal storage and the app's smartphone storage, with a focus on encryption mechanisms for locally stored data and internet communication. During this analysis, we spotted a critical vulnerability that enables attackers to manipulate their insurance scores and potentially reduce premiums by up to 30\%. Our model and real-world analysis provide a solid foundation for reducing the risk of such manipulation of insurance premiums.}, language = {en} } @unpublished{LoewVasuAshokaHutzelmannetal.2025, author = {L{\"o}w, Jakob and Vasu Ashoka, Vishwa Vimukthi and Hutzelmann, Thomas and Hof, Hans-Joachim}, title = {Security Aspects of ISO 15118 Plug and Charge Payment}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2512.15966}, year = {2025}, language = {en} }