TY - CHAP A1 - Aslansefat, Koorosh A1 - Sorokos, Ioannis A1 - Whiting, Declan A1 - Tavakoli Kolagari, Ramin A1 - Papadopoulos, Yiannis T1 - SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures T2 - Model-Based Safety and Assessment Y1 - 2020 SN - 9783030589196 U6 - https://doi.org/10.1007/978-3-030-58920-2_13 SN - 0302-9743 SP - 197 EP - 211 PB - Springer International Publishing CY - Cham ER - TY - CHAP A1 - Wägemann, Tobias A1 - Tavakoli Kolagari, Ramin A1 - Schmid, Klaus T1 - Optimal Product Line Architectures for the Automotive Industry T2 - Modellierung 2018 Y1 - 2018 SN - 9783885796749 CY - Bonn ER - TY - CHAP A1 - Reiser, Mark-Oliver A1 - Tavakoli Kolagari, Ramin A1 - Weber, Matthias T1 - Manifoldness of Variability Modeling — Considering the Potential for Further Integration T2 - Lecture Notes in Computer Science N2 - Variability management has become an important concern in software and systems engineering. Especially in industrial settings a rigid management of variability has been identified as an important prerequisite for further optimization of the development process, e.g. for reuse of software sub-systems across vehicle models such as the Mercedes Benz A-Class and C-Class. In response to this growing practical interest, the scientific community has come up with numerous concepts and techniques for modeling variability. However, despite initial attempts to integrate or unify some of these manifold approaches, a clear understanding of how they precisely relate to each other is still not yet achieved. In the paper, various techniques for variability modeling are elaborated and a basic classification scheme is proposed. From this we derive their common capabilities, which arguably embody the essence of variability modeling in general. On this basis, a discussion is presented that concerns the potential and feasibility of integrating all these diverse techniques into a single, common technique for variability modeling. Y1 - 2008 SN - 9783540852780 U6 - https://doi.org/10.1007/978-3-540-85279-7_23 SN - 0302-9743 SP - 291 EP - 303 PB - Springer Berlin Heidelberg CY - Berlin, Heidelberg ER - TY - CHAP A1 - Espinoza, Huascar A1 - Gérard, Sébastien A1 - Lönn, Henrik A1 - Tavakoli Kolagari, Ramin T1 - Harmonizing MARTE, EAST-ADL2, and AUTOSAR to Improve the Modelling of Automotive Systems T2 - Proceedings of the 1st Workshop on the Definition, Evaluation, and Exploitation of Modelling and Computing Standards for Real-Time Embedded Systems (STANDRTS’09) Y1 - 2009 ER - TY - CHAP A1 - Bergler, Matthias A1 - Tavakoli Kolagari, Ramin T1 - Automotive Software Security Engineering based on the ISO 21434 T2 - ACM Computing Surveys N2 - The increasing use of software and connectivity in modern vehicles has made cybersecurity an important issue in the automotive industry. ISO 21434 is a standard for automotive cybersecurity engineering that provides guidelines for the development and validation of secure automotive systems. For effective implementation and practical use of ISO 21434, it must be incorporated into existing automotive industry development workflows. In this paper, we investigate the practical applicability of ISO 21434 in the context of the Security Abstraction Model (SAM), a security modeling approach for the domain-specific modeling language EAST-ADL, and provide insights into the benefits and consequences of this approach. In doing so, we describe the methodological opportunities of integrating ISO 21434 into SAM on the one hand and present a case study illustrating the application of this integrated approach in the development of a secure automotive system on the other hand. Our results suggest that the integration of ISO 21434 into SAM better supports automotive system security in the early development phases and makes it transparent to a wide range of stakeholders. At the same time, it becomes clear that a representation of the interrelationships in the form of a metamodel, in contrast to ISO 21434 in which these are only described textually, significantly improves conceptual understanding and ultimately enables pragmatic usability in industrial development. KW - automotive KW - software engineering KW - cybersecurity Y1 - 2023 SN - 979-8-4007-0805-3 U6 - https://doi.org/10.1145/3631991.3631994 SP - 17 EP - 26 PB - Association for Computing Machinery (ACM) CY - Tokyo, Japan ER - TY - CHAP A1 - Alexander, Ian F. A1 - Tavakoli Kolagari, Ramin ED - Alexander, Ian F. ED - Maiden, Neil T1 - PUTTING SCENARIOS INTO PRACTICE T2 - Scenarios, Stories, Use Cases: Through the Systems Development Life-Cycle Y1 - 2004 SN - 9780470861943 PB - Wiley ER - TY - CHAP A1 - van Wagensveld, Remko A1 - Wägemann, Tobias A1 - Hehenkamp, Niklas A1 - Tavakoli Kolagari, Ramin A1 - Margull, Ulrich A1 - Mader, Ralph T1 - Intra-Task Parallelism in Automotive Real-Time Systems T2 - Proceedings of the 9th International Workshop on Programming Models and Applications for Multicores and Manycores Y1 - 2018 U6 - https://doi.org/10.1145/3178442.3178449 SP - 61 EP - 70 PB - ACM CY - New York, NY, USA ER - TY - JOUR A1 - van Wagensveld, Remko A1 - Wägemann, Tobias A1 - Mader, Ralph A1 - Tavakoli Kolagari, Ramin A1 - Margull, Ulrich T1 - Evaluation and modeling of the supercore parallelization pattern in automotive real-time systems JF - Parallel Computing Y1 - 2019 U6 - https://doi.org/10.1016/j.parco.2018.12.004 SN - 0167-8191 VL - 81 SP - 122 EP - 130 PB - Elsevier BV ER - TY - CHAP A1 - Freiling, Felix A1 - Tavakoli Kolagari, Ramin A1 - Auernhammer, Katja T1 - Efficient Black-Box Search for Adversarial Examples using Relevance Masks T2 - Proceedings of the 2020 Workshop on DYnamic and Novel Advances in Machine Learning and Intelligent Cyber Security N2 - Machine learning classifiers for image recognition are prevalent in many applications. We study the problem of finding adversarial examples for such classifiers, i.e., to manipulate the images in such a way that they still look like the original images to a human but are misinterpreted by the classifier. Finding adversarial examples corresponds to a search problem in the image space. We focus on black-box attacks that can only use the original classifier to guide the search. The challenge is not to find adversarial examples, but rather to find them efficiently, ideally in real time. We show two novel methods that increase the efficiency of black-box search algorithms for adversarial examples: The first uses a relevance mask, i.e., a bitmask on the original image that restricts the search to those pixels that appear to be more relevant to the attacked classifier than others. The second exploits the discovery of merge drift, a phenomenon that negatively affects search algorithms that are based on the merging of image candidates. We evaluate both concepts on existing and new algorithms. KW - Machine Learning, Attacks, Adversarial Examples, Relevance Masks Y1 - 2022 U6 - https://doi.org/10.1145/3477997.3478013 SP - 1 EP - 12 PB - ACM CY - New York, NY, USA ER - TY - CHAP A1 - Tavakoli Kolagari, Ramin A1 - Raschke, Alexander A1 - Schneiderhan, Matthias A1 - Alexander, Ian T1 - Entscheidungsdokumentation bei der Entwicklung innovativer Systeme für produktlinien-basierte Entwicklungsprozesse T2 - Ulmer Informatik Berichte Y1 - 2008 PB - University of Ulm CY - Ulm ER - TY - CHAP A1 - Tavakoli Kolagari, Ramin A1 - Raschke, Alexander T1 - Vergleich von klassischer und agiler Entwicklungsmethodik anhand der Entwicklung eines in Statemate simulierbaren Modells eines Türsteuergeräts T2 - Softwaretechnik-Trends Y1 - 2004 ER - TY - JOUR A1 - Zoppelt, Markus A1 - Tavakoli Kolagari, Ramin T1 - Reaching Grey Havens: Industrial Automotive Security Modeling with SAM JF - International Journal on Advances in Security Y1 - 2018 VL - 11 PB - IARIA ER - TY - CHAP A1 - Fischer, Alexander A1 - Tolvanen, Juha-Pekka A1 - Tavakoli Kolagari, Ramin T1 - Automotive Cybersecurity Engineering with Modeling Support T2 - Annals of Computer Science and Information Systems N2 - Rapid advances of connected and autonomous vehicle technology have led to an increase in cyber-attacks. This in turn has driven the development of the ISO 21434 standard aimed at supporting the management of cybersecurity risks in the automotive industry. There is, however, a disconnect between the standard and the currently applied model-based development approaches that are increasingly applied for systems and software development. In this paper, we present tool support created for model-based automotive cybersecurity engineering. This tool is built upon the existing automotive systems development language, EAST-ADL, with extensions to address security in accordance with the ISO 21434 standard covering modeling support, calculation of security-related metrics such as impact, risk, and attack feasibility, and generation of ISO 21434 compliant security threat reports. Meeting the requirements of cybersecurity engineeering according to ISO 21434 are demonstrated with two examples. Y1 - 2024 SN - 978-83-969601-6-0 U6 - https://doi.org/10.15439/2024F5017 SN - 2300-5963 VL - 39 SP - 319 EP - 329 PB - IEEE ER - TY - CHAP A1 - Ballhause, Christoph A1 - Tavakoli Kolagari, Ramin T1 - Hardware-unabhängige Spezifikation von Steuergeräte-Software T2 - Softwaretechnik-Trends Y1 - 2004 ER - TY - CHAP A1 - Fischer, Alexander A1 - Tavakoli Kolagari, Ramin T1 - Harmonization of Security Standards for Embedded Systems in a Comprehensive Metamodel: The Security Abstraction Model T2 - 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C) N2 - The development of reliable systems is inextricably linked to the consideration of security aspects during the development process. Especially in the context of autonomous vehicles, security is crucial to ensure user trust in this technology. This work presents an overview of the harmonization of the Security Abstration Model (SAM). The results demonstrate that our approach is compliant with relevant security standards for embedded systems, indicating compliance and robustness. The utilization of SAM improves conceptual understanding and promotes clarity in security engineering. Y1 - 2024 SN - 979-8-3503-6565-8 U6 - https://doi.org/10.1109/QRS-C63300.2024 SP - 1314 EP - 1315 PB - IEEE ER - TY - CHAP A1 - Auernhammer, Katja A1 - Tavakoli Kolagari, Ramin T1 - Workshop for Automotive Software Systems Engineering Education T2 - 2020 IEEE 32nd Conference on Software Engineering Education and Training (CSEE&T) N2 - In view of the advent of autonomous driving, the automotive industry is longing for adequately trained employees in a wide range of fields; these include not so much the classic automotive engineering and embedded systems topics, but rather software development, robotics, machine learning, statistics and design. As a particularly specific, industry-oriented discipline, automotive software engineering is hardly represented in academic teaching, except for a few AUTOSAR courses. But as a beacon project of digitization, autonomous driving can arouse broad interest among students who are interested in how diverse disciplines can be profitably combined to realize an impressive project. This interest is an excellent chance for both universities and industry to engage in constructive dialogue, to carry out exciting multidisciplinary projects at universities and to meet the needs of industry with graduates who have a relevant and versatile education. This workshop provides an opportunity for both sides to meet, share requirements and experiences and develop a best practice course according to industry requirements and academic feasibility. KW - Automotive engineering , Software engineering , Industries , Education , Conferences , Machine learning , Automobiles Y1 - 2020 U6 - https://doi.org/10.1109/CSEET49119.2020.9206169 PB - IEEE ER - TY - CHAP A1 - Zoppelt, Markus A1 - Tavakoli Kolagari, Ramin T1 - SAM: A Security Abstraction Model for Automotive Software Systems T2 - Lecture Notes in Computer Science Y1 - 2019 SN - 9783030168735 U6 - https://doi.org/10.1007/978-3-030-16874-2_5 SN - 0302-9743 SP - 59 EP - 74 PB - Springer International Publishing CY - Cham ER - TY - CHAP A1 - Wägemann, Tobias A1 - Tavakoli Kolagari, Ramin A1 - Schmid, Klaus T1 - ADOOPLA - Combining Product-Line- and Product-Level Criteria in Multi-objective Optimization of Product Line Architectures T2 - Lecture Notes in Computer Science Y1 - 2019 SN - 9783030299828 U6 - https://doi.org/10.1007/978-3-030-29983-5_9 SN - 0302-9743 SP - 126 EP - 142 PB - Springer International Publishing CY - Cham ER - TY - CHAP A1 - Wagemann, Tobias A1 - Tavakoli Kolagari, Ramin A1 - Schmid, Klaus T1 - Exploring Automotive Stakeholder Requirements for Architecture Optimization Support T2 - 2019 IEEE International Conference on Software Architecture Companion (ICSA-C) Y1 - 2019 U6 - https://doi.org/10.1109/ICSA-C.2019.00015 SP - 37 EP - 44 PB - IEEE ER - TY - CHAP A1 - Wägemann, Tobias A1 - Langer, Tobias A1 - Mottok, Jürgen A1 - Osinski, Lukas A1 - Stappert, Friedhelm A1 - Tavakoli Kolagari, Ramin T1 - Models for Dependable Heterogenous Multi- and Many-Core System Software Design Revisited T2 - 29th International Conference on Architecture of Computing Systems Y1 - 2016 ER - TY - CHAP A1 - Auernhammer, Katja A1 - Tavakoli Kolagari, Ramin A1 - Zoppelt, Markus T1 - Attacks on machine learning: Lurking danger for accountability T2 - Proceedings of the 2019 AAAI Workshop on Artificial Intelligence Safety, SafeAI 2019 Y1 - 2019 VL - 2301 PB - CEUR-WS ER - TY - CHAP A1 - Cuenot, Philippe A1 - Frey, Patrick A1 - Johansson, Rolf A1 - Lönn, Henrik A1 - Papadopoulos, Yiannis A1 - Reiser, Mark-Oliver A1 - Sandberg, Anders A1 - Servat, David A1 - Tavakoli Kolagari, Ramin A1 - Törngren, Martin A1 - Weber, Matthias T1 - 11 The EAST-ADL Architecture Description Language for Automotive Embedded Software T2 - Lecture Notes in Computer Science N2 - Current trends in automotive embedded systems focus on how to manage the increasing software content, with a strong emphasis on standardization of the embedded software structure. The management of engineering information remains a critical challenge in order to support development and other stages of the life-cycle. System modelling based on an Architecture Description Language (ADL) is a way to keep these assets within one information structure. This paper presents the EAST-ADL2 modelling language, developed in the ITEA EAST-EEA project and further enhanced in the ATESST project (www.atesst.org). EAST-ADL2 supports comprehensive model-based development of embedded systems and provides dedicated constructs to facilitate variability and product line management, requirements engineering, representation of functional as well as software/hardware solutions, and timing and safety analysis. Y1 - 2010 SN - 9783642162763 U6 - https://doi.org/10.1007/978-3-642-16277-0_11 SN - 0302-9743 SP - 297 EP - 307 PB - Springer Berlin Heidelberg CY - Berlin, Heidelberg ER - TY - CHAP A1 - Bergler, Matthias A1 - Tolvanen, Juha-Pekka A1 - Tavakoli Kolagari, Ramin T1 - Integrating Security and Safety with Systems Engineering: a Model-Based Approach N2 - Development of reliable systems requires that safety and security concerns are acknowledged during system development. Adding them afterwards is risky as many concerns are missed if not elicited together with the system requirements. Unfortunately, languages for systems engineering, like SysML, typically ignore security and safety forcing development teams to split the work into different formats, languages and tools without easy collaboration, with limited traceability, separate versioning and restricted use of automation that tools can provide. We present a model-based approach targeting automotive that integrates safety and security aspects with other system development practices. This is achieved via a comprehensive domain-specific modeling language that is extendable by language users. We demonstrate this approach with practical examples on how security and safety concerns are recognized along with traditional system design and analysis phases. KW - model-based development KW - security KW - safety KW - domainspecific language KW - system engineering KW - software engineering Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:92-opus4-14863 ER - TY - CHAP A1 - Bergler, Matthias A1 - Tavakoli Kolagari, Ramin A1 - Lundqvist, Kristina T1 - Case study on the use of the SafeML approach in training autonomous driving vehicles N2 - The development quality for the control software for autonomous vehicles is rapidly progressing, so that the control units in the field generally perform very reliably. Nevertheless, fatal misjudgments occasionally occur putting people at risk: such as the recent accident in which a Tesla vehicle in Autopilot mode rammed a police vehicle. Since the object recognition software which is a part of the control software is based on machine learning (ML) algorithms at its core, one can distinguish a training phase from a deployment phase of the software. In this paper we investigate to what extent the deployment phase has an impact on the robustness and reliability of the software; because just as traditional, software based on ML degrades with time. A widely known effect is the so-called concept drift: in this case, one finds that the deployment conditions in the field have changed and the software, based on the outdated training data, no longer responds adequately to the current field situation. In a previous research paper, we developed the SafeML approach with colleagues from the University of Hull, where datasets are compared for their statistical distance measures. In doing so, we detected that for simple, benchmark data, the statistical distance correlates with the classification accuracy in the field. The contribution of this paper is to analyze the applicability of the SafeML approach to complex, multidimensional data used in autonomous driving. In our analysis, we found that the SafeML approach can be used for this data as well. In practice, this would mean that a vehicle could constantly check itself and detect concept drift situation early. KW - Automotive KW - Safety KW - SafeML KW - Machine Learning KW - Autonomous Driving Y1 - 2022 ER - TY - CHAP A1 - Zoppelt, Markus A1 - Tavakoli Kolagari, Ramin T1 - What Today's Serious Cyber Attacks on Cars Tell Us BT - Consequences for Automotive Security and Dependability T2 - Model-Based Safety and Assessment : 6th International Symposium, IMBSA 2019, Thessaloniki, Greece, October 16–18, 2019, Proceedings N2 - Highly connected with the environment via various interfaces, cars have been the focus of malicious cyber attacks for years. These attacks are becoming an increasing burden for a society with growing vehicle autonomization: they are the sword of Damocles of future mobility. Therefore, research is particularly active in the area of vehicle IT security, and in part also in the area of dependability, in order to develop effective countermeasures and to maintain a minimum of one step ahead of hackers. This paper examines the known state-of-the-art security and dependability measures based on a detailed and systematic analysis of published cyber attacks on automotive software systems. The sobering result of the analysis of the cyber attacks with the model-based technique SAM (Security Abstraction Model) and a categorization of the examined attacks in relation to the known security and dependability measures is that most countermeasures against cyber attacks are hardly effective. They either are not applicable to the underlying problem or take effect too late; the intruder has already gained access to a substantial part of the vehicle when the countermeasures apply. The paper is thus contributing to an understanding of the gaps that exist today in the area of vehicle security and dependability and concludes concrete research challenges. KW - Automotive Security KW - Automotive System Architecture KW - Dependability KW - Model-Driven Engineering Methodologies Y1 - 2019 SN - 978-3-030-32872-6 U6 - https://doi.org/10.1007/978-3-030-32872-6_18 SP - 219 EP - 285 PB - Springer CY - Cham ER - TY - CHAP A1 - Zoppelt, Markus A1 - Tavakoli Kolagari, Ramin T1 - UnCle SAM: Modeling Cloud Attacks with the Automotive Security Abstraction Model T2 - CLOUD COMPUTING 2019 : The Tenth International Conference on Cloud Computing, GRIDs, and Virtualization N2 - Driverless (autonomous) vehicles will have greater attack potential than any other individual mobility vehicles ever before. Most intelligent vehicles require communication interfaces to the environment, direct connections (e.g., Vehicle-to-X (V2X)) to an Original Equipment Manufacturer (OEM) backend service or a cloud. By connecting to the Internet, which is not only necessary for the infotainment systems, cars could increasingly turn into targets for malware or botnet attacks. Remote control via the Internet by a remote attacker is also conceivable, as has already been impressively demonstrated. This paper examines security modeling for cloud-based remote attacks on autonomous vehicles using a Security Abstraction Model (SAM) for automotive software systems). SAM adds to the early phases of (automotive) software architecture development by explicitly documenting attacks and handling them with security techniques. SAM also provides the basis for comprehensive security analysis techniques, such as the already available Common Vulnerability Scoring System (CVSS) or any other attack assessment system. KW - Automotive Security KW - Automotive Software Engineering KW - Security Modeling KW - Cloud Attacks KW - OTA Updates Y1 - 2019 SN - 978-1-61208-703-0 ER - TY - CHAP A1 - Bergler, Matthias A1 - Tolvanen, Juha-Pekka A1 - Zoppelt, Markus A1 - Tavakoli Kolagari, Ramin T1 - Social Engineering Exploits in Automotive Software Security BT - Modeling Human-targeted Attacks with SAM T2 - Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021) N2 - Security cannot be implemented into a system retrospectively without considerable effort, so security must be taken into consideration already at the beginning of the system development. The engineering of automotive software is by no means an exception to this rule. For addressing automotive security, the AUTOSAR and EAST-ADL standards for domain-specific system and component modeling provide the central foundation as a start. The EASTADL extension SAM enables fully integrated security modeling for traditional feature-targeted attacks. Due to the COVID-19 pandemic, the number of cyber-attacks has increased tremendously and of these, about 98 percent are based on social engineering attacks. These social engineering attacks exploit vulnerabilities in human behaviors, rather than vulnerabilities in a system, to inflict damage. And these social engineering attacks also play a relevant but nonetheless regularly neglected role for automotive software. The contribution of this paper is a novel modeling concept for social engineering attacks and their criticality assessment integrated into a general automotive software security modeling approach. This makes it possible to relate social engineering exploits with feature-related attacks. To elevate the practical usage, we implemented an integration of this concept into the established, domain-specific modeling tool MetaEdit+. The tool support enables collaboration between stakeholders, calculates vulnerability scores, and enables the specification of security objectives and measures to eliminate vulnerabilities. KW - automotive systems, social engineering attacks, design, model-based development, modeling, security Y1 - 2021 U6 - https://doi.org/10.3850/978-981-18-2016-8_720-cd SP - 2502 EP - 2509 PB - Research Publishing Services CY - Singapore ER - 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 - Fischer, Alexander A1 - Tavakoli Kolagari, Ramin T1 - Towards Robust Autonomous Vehicle Software: Extending SAM for a Holistic Security Analysis T2 - 2024 8th International Conference on System Reliability and Safety (ICSRS) N2 - The Security Abstraction Model (SAM) is a meta-model that enables comprehensive security modeling and does not only consider technological aspects, but also takes into consideration the influence of social engineering. This plays an important role by exploiting vulnerabilities in human behavior and thus represents a dimension that must not be neglected in possible attacks on vehicle systems. This work is dedicated to the advancement of the Security Abstraction Model (SAM) with the aim of enabling improved modelling and analysis of vulnerabilities in the context of software and system security; the relevant standards and best practices of the international security literature for embedded systems are harmonized in one metamodel. By extending SAM, this research enables a more nuanced understanding and representation of vulnerabilities, attacks and scenarios, improving the effectiveness of security analysis and remediation strategies. The integration of different perspectives, including technological, social and behavioral factors, underlines the comprehensive nature of the proposed model. This interdisciplinary approach recognizes the complexity of security threats in autonomous vehicle systems and enables stakeholders to develop more effective countermeasures. This work presents an overview of the extension of the Security Abstration Model (SAM) and evaluates this security technology through expert interviews. The results demonstrate that our approach is compliant not only in the automotive domain, but also with standards for embedded systems from other domains. It thus provides a comprehensive and universal approach for the development of secure systems. Y1 - 2024 SN - 979-8-3503-5450-8 U6 - https://doi.org/10.1109/ICSRS63046.2024.10927601 SP - 578 EP - 586 PB - IEEE ER - TY - CHAP A1 - Arzberger, Alexandra A1 - Tavakoli Kolagari, Ramin T1 - Conceptional Guidelines for Robustness Evaluation of Autonomous Agents T2 - SE2026 – Companion Proceedings N2 - Autonomous vehicles rely on high-quality sensor data, such as from camera and Light Detection and Ranging (LiDAR) for their driving decisions. Object detection and motion planning are core tasks in autonomous driving, both requiring robustness against diverse sensor perturbations. While robustness evaluation for object detection is well studied, far fewer approaches exist for end-to-end (E2E) motion planning agents. This paper outlines key differences between object detection and E2E motion planning and discusses resulting challenges for robustness evaluation. We present an empirical study comparing the robustness of the object detection system LoGoNet and the E2E agent TransFuser++, both using camera and LiDAR inputs, under LiDAR-only perturbations. Based on our findings, we propose requirements and a parameterization scheme for perturbations to guide the design of future robustness evaluation frameworks for autonomous agents. KW - autonomous driving KW - end-to-end models KW - object detection KW - robustness Y1 - 2026 U6 - https://doi.org/10.18420/se2026-ws_01 PB - Gesellschaft für Informatik CY - Bonn 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 -