@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selected samples from edge case classifications by domain experts is vital for assessing the IDSs.}, language = {en} } @unpublished{PetrovskaErjiageKugele2025, author = {Petrovska, Ana and Erjiage, Guan and Kugele, Stefan}, title = {Defining Self-adaptive Systems: A Systematic Literature Review}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2505.17798}, year = {2025}, abstract = {In the last two decades, the popularity of self-adaptive systems in the field of software and systems engineering has drastically increased. However, despite the extensive work on self-adaptive systems, the literature still lacks a common agreement on the definition of these systems. To this day, the notion of self-adaptive systems is mainly used intuitively without a precise understanding of the terminology. Using terminology only by intuition does not suffice, especially in engineering and science, where a more rigorous definition is necessary. In this paper, we investigate the existing formal definitions of self-adaptive systems and how these systems are characterised across the literature. Additionally, we analyse and summarise the limitations of the existing formal definitions in order to understand why none of the existing formal definitions is used more broadly by the community. To achieve this, we have conducted a systematic literature review in which we have analysed over 1400 papers related to self-adaptive systems. Concretely, from an initial pool of 1493 papers, we have selected 314 relevant papers, which resulted in nine primary studies whose primary objective was to define self-adaptive systems formally. Our systematic review reveals that although there has been an increasing interest in self-adaptive systems over the years, there is a scarcity of efforts to define these systems formally. Finally, as part of this paper, based on the analysed primary studies, we also elicit requirements and set a foundation for a potential (formal) definition in the future that is accepted by the community on a broader range.}, 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{KugelePetrovskaGerostathopoulos2021, author = {Kugele, Stefan and Petrovska, Ana and Gerostathopoulos, Ilias}, title = {Towards a Taxonomy of Autonomous Systems}, booktitle = {Software Architecture: 15th European Conference, ECSA 2021; Virtual Event, Sweden, September 13-17, 2021: Proceedings}, editor = {Biffl, Stefan and Navarro, Elena and L{\"o}we, Welf and Sirjani, Marjan and Mirandola, Raffaela and Weyns, Danny}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-86044-8}, doi = {https://doi.org/10.1007/978-3-030-86044-8_3}, pages = {37 -- 45}, year = {2021}, 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} }