@inproceedings{AuernhammerTavakoliKolagariZoppelt2019, author = {Auernhammer, Katja and Tavakoli Kolagari, Ramin and Zoppelt, Markus}, title = {Attacks on machine learning: Lurking danger for accountability}, series = {Proceedings of the 2019 AAAI Workshop on Artificial Intelligence Safety, SafeAI 2019}, volume = {2301}, booktitle = {Proceedings of the 2019 AAAI Workshop on Artificial Intelligence Safety, SafeAI 2019}, publisher = {CEUR-WS}, year = {2019}, language = {en} } @incollection{CuenotFreyJohanssonetal.2010, author = {Cuenot, Philippe and Frey, Patrick and Johansson, Rolf and L{\"o}nn, Henrik and Papadopoulos, Yiannis and Reiser, Mark-Oliver and Sandberg, Anders and Servat, David and Tavakoli Kolagari, Ramin and T{\"o}rngren, Martin and Weber, Matthias}, title = {11 The EAST-ADL Architecture Description Language for Automotive Embedded Software}, series = {Lecture Notes in Computer Science}, booktitle = {Lecture Notes in Computer Science}, publisher = {Springer Berlin Heidelberg}, address = {Berlin, Heidelberg}, isbn = {9783642162763}, issn = {0302-9743}, doi = {10.1007/978-3-642-16277-0_11}, pages = {297 -- 307}, year = {2010}, abstract = {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.}, language = {en} } @inproceedings{BerglerTolvanenTavakoliKolagari2022, author = {Bergler, Matthias and Tolvanen, Juha-Pekka and Tavakoli Kolagari, Ramin}, title = {Integrating Security and Safety with Systems Engineering: a Model-Based Approach}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:92-opus4-14863}, pages = {13}, year = {2022}, abstract = {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.}, language = {en} } @inproceedings{BerglerTavakoliKolagariLundqvist2022, author = {Bergler, Matthias and Tavakoli Kolagari, Ramin and Lundqvist, Kristina}, title = {Case study on the use of the SafeML approach in training autonomous driving vehicles}, pages = {11}, year = {2022}, abstract = {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.}, language = {en} } @inproceedings{ZoppeltTavakoliKolagari2019, author = {Zoppelt, Markus and Tavakoli Kolagari, Ramin}, title = {What Today's Serious Cyber Attacks on Cars Tell Us}, series = {Model-Based Safety and Assessment : 6th International Symposium, IMBSA 2019, Thessaloniki, Greece, October 16-18, 2019, Proceedings}, booktitle = {Model-Based Safety and Assessment : 6th International Symposium, IMBSA 2019, Thessaloniki, Greece, October 16-18, 2019, Proceedings}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-32872-6}, doi = {10.1007/978-3-030-32872-6_18}, pages = {219 -- 285}, year = {2019}, abstract = {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.}, language = {en} } @inproceedings{ZoppeltTavakoliKolagari2019, author = {Zoppelt, Markus and Tavakoli Kolagari, Ramin}, title = {UnCle SAM: Modeling Cloud Attacks with the Automotive Security Abstraction Model}, series = {CLOUD COMPUTING 2019 : The Tenth International Conference on Cloud Computing, GRIDs, and Virtualization}, booktitle = {CLOUD COMPUTING 2019 : The Tenth International Conference on Cloud Computing, GRIDs, and Virtualization}, isbn = {978-1-61208-703-0}, pages = {6}, year = {2019}, abstract = {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.}, language = {en} } @inproceedings{BerglerTolvanenZoppeltetal.2021, author = {Bergler, Matthias and Tolvanen, Juha-Pekka and Zoppelt, Markus and Tavakoli Kolagari, Ramin}, title = {Social Engineering Exploits in Automotive Software Security}, series = {Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)}, booktitle = {Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)}, publisher = {Research Publishing Services}, address = {Singapore}, doi = {10.3850/978-981-18-2016-8_720-cd}, pages = {2502 -- 2509}, year = {2021}, abstract = {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.}, language = {en} } @inproceedings{ArzbergerTavakoliKolagari2025, author = {Arzberger, Alexandra and Tavakoli Kolagari, Ramin}, title = {Hi-ALPS - An Experimental Robustness Quantification of Six LiDAR-based Object Detection Systems for Autonomous Driving}, series = {2025 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)}, booktitle = {2025 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)}, publisher = {IEEE}, doi = {10.1109/SaTML64287.2025.00050}, pages = {811 -- 823}, year = {2025}, abstract = {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.}, language = {en} } @incollection{FischerTolvanenTavakoliKolagari2025, author = {Fischer, Alexander and Tolvanen, Juha-Pekka and Tavakoli Kolagari, Ramin}, title = {Embedded Systems Security Co-design: Modeling Support for Managers and Developers}, series = {Lecture Notes in Business Information Processing}, booktitle = {Lecture Notes in Business Information Processing}, publisher = {Springer Nature Switzerland}, address = {Cham}, isbn = {978-3-031-84913-8}, issn = {1865-1348}, doi = {10.1007/978-3-031-84913-8_8}, pages = {206 -- 232}, year = {2025}, abstract = {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.}, language = {en} } @inproceedings{FischerBurkTavakoliKolagarietal.2025, author = {Fischer, Alexander and Burk, Louis and Tavakoli Kolagari, Ramin and Wienkop, Uwe}, title = {Machine-Readable by Design: Language Specifications as the Key to Integrating LLMs into Industrial Tools}, series = {Annals of Computer Science and Information Systems}, volume = {43}, booktitle = {Annals of Computer Science and Information Systems}, publisher = {IEEE}, isbn = {978-83-973291-6-4}, issn = {2300-5963}, doi = {10.15439/2025F5613}, pages = {531 -- 542}, year = {2025}, abstract = {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.}, language = {en} }