TY - CHAP A1 - Fischer, Alexander A1 - Tolvanen, Juha-Pekka A1 - Tavakoli Kolagari, Ramin T1 - Embedded Systems Security Co-design: Modeling Support for Managers and Developers T2 - Lecture Notes in Business Information Processing N2 - The proliferation of connected and autonomous vehicle technologies has significantly increased cybersecurity risks. Modern vehicles, as complex and networked computer systems, require comprehensive protection against malicious external attacks, much like conventional computers. Addressing these challenges requires robust tools that align established automotive model-based development approaches with the ISO/SAE 21434 standard for automotive cybersecurity, which became mandatory following its publication in 2021. Building on prior research, this paper introduces key innovations in the conceptual framework and the tool support that integrate seamlessly into existing automotive development methodologies. These advancements are rooted in extensions to the Security Abstraction Model (SAM) informed by the ISO/SAE 21434 standard. Notably, SAM now incorporates advanced methods for score calculation, including an attack potential-based approach for assessing attack feasibility and the computation of risk scores using risk matrices. Usability improvements are also a contribution, achieved through the introduction of BPMN-style (Business Process Model and Notation) diagrams tailored for the accessible visualization of otherwise complex security models. These diagrams make multifaceted attack trees easier to interpret, enabling managers and other non-technical stakeholders to intuitively understand security vulnerabilities and make informed decisions. Additionally, the tool supports updated metrics for impact and risk analysis, demonstrated through practical applications involving automotive subsystems such as braking. These examples illustrate improved traceability between SAM and functional design, ensuring that cybersecurity requirements are effectively integrated into the broader development lifecycle. Y1 - 2025 SN - 978-3-031-84913-8 U6 - https://doi.org/10.1007/978-3-031-84913-8_8 SN - 1865-1348 SP - 206 EP - 232 PB - Springer Nature Switzerland CY - Cham ER - TY - CHAP A1 - Fischer, Alexander A1 - Burk, Louis A1 - Tavakoli Kolagari, Ramin A1 - Wienkop, Uwe T1 - Machine-Readable by Design: Language Specifications as the Key to Integrating LLMs into Industrial Tools T2 - Annals of Computer Science and Information Systems N2 - We propose a meta-language-based approach enabling Large Language Models (LLMs) to reliably generate structured, machine-readable artifacts referred to as Meta-Language-defined Structures (MLDS) adapted to domain requirements, without adhering strictly to standard formats like JSON or XML. By embedding explicit schema instructions within prompts, we evaluated the method across diverse use cases, including automated Virtual Reality environment generation and automotive security modeling. Our experiments demonstrate that the meta-language approach significantly improves LLM-generated structure compliance, with an 88 % validation rate across 132 test scenarios. Compared to traditional methods using LangChain and Pydantic, our MLDS method reduces setup complexity by approximately 80 %, despite a marginally higher error rate. Furthermore, the MLDS artifacts produced were easily editable, enabling rapid iterative refinement. This flexibility greatly alleviates the “blank page syndrome” by providing structured initial artifacts suitable for immediate use or further human enhancement, making our approach highly practical for rapid prototyping and integration into complex industrial workflows. Y1 - 2025 SN - 978-83-973291-6-4 U6 - https://doi.org/10.15439/2025F5613 SN - 2300-5963 VL - 43 SP - 531 EP - 542 PB - IEEE ER - TY - CHAP A1 - Arzberger, Alexandra A1 - Tavakoli Kolagari, Ramin T1 - Hi-ALPS - An Experimental Robustness Quantification of Six LiDAR-based Object Detection Systems for Autonomous Driving T2 - 2025 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) N2 - Light Detection and Ranging (LiDAR) is an essential sensor technology for autonomous driving as it can capture high-resolution 3D data. As 3D object detection systems (OD) can interpret such point cloud data, they play a key role in the driving decisions of autonomous vehicles. Consequently, such 3D OD must be robust against all types of perturbations and must therefore be extensively tested. One approach is the use of adversarial examples, which are small, sometimes sophisticated perturbations in the input data that change, i.e., falsify, the prediction of the OD. These perturbations are carefully designed based on the weaknesses of the OD. The robustness of the OD cannot be quantified with adversarial examples in general, because if the OD is vulnerable to a given attack, it is unclear whether this is due to the robustness of the OD or whether the attack algorithm produces particularly strong adversarial examples. The contribution of this work is Hi-ALPS -- Hierarchical Adversarial-example-based LiDAR Perturbation Level System, where higher robustness of the OD is required to withstand the perturbations as the perturbation levels increase. In doing so, the Hi-ALPS levels successively implement a heuristic followed by established adversarial example approaches. In a series of comprehensive experiments using Hi-ALPS, we quantify the robustness of six state-of-the-art 3D OD under different types of perturbations. The results of the experiments show that none of the OD is robust against all Hi-ALPS levels; an important factor for the ranking is that human observers can still correctly recognize the perturbed objects, as the respective perturbations are small. To increase the robustness of the OD, we discuss the applicability of state-of-the-art countermeasures. In addition, we derive further suggestions for countermeasures based on our experimental results. Y1 - 2025 U6 - https://doi.org/10.1109/SaTML64287.2025.00050 SP - 811 EP - 823 PB - IEEE ER -