Thesis Topics

On this page, you will find a list of available thesis topics that are available in our institute. Information about on-going and past theses can be found on this page. Some of the thesis descriptions are in German.

Note that because many of our topics are issued in German, some of the descriptions on this page are also German only. We are currently working on providing complete translations.

Open Theses

“Re-Implementing Zookeeper using an SMR framework,” Bachelor Thesis, Project, F. J. Hauck (Supervisor), F. J. Hauck (Examiner), Inst. of Distr. Sys., Ulm Univ., 2026 – Open.
State-machine replication is a concept to achieve fault tolerance. There are several frameworks to support SMR-based applications. Zookeeper is an application implementing a so-called coordination service. It is internally build with SMR technology, but does not use an underlying framework. The task of this work is to reimplement Zookeeper with the BFT-SMaRt/SMRteez framework. The goal is to demonstrate that the framework can handle such applications. In case of a Bachelor's thesis, in case of remaining time also in case of project work, the performance shall be compared to the original Zookeeper implementation. For the implementation it can be expected that at least some code can be reused from the original implementation.
“Development of an execution-layer interface for an SMR framework,” Master's thesis, F. J. Hauck (Supervisor), F. J. Hauck (Examiner), Inst. of Distr. Sys., Ulm Univ., 2026 – Open.
State-machine replication is a concept to achieve fault tolerance. There are several frameworks to support SMR-based applications including BFT-SMaRt/SMRteez. In this framework the interface to the layer that actually executes the application is somewhat tangled due to its history and due to bad abstractions. The goal of this work is to develop a modern and efficient API to separate the execution layer, responsible for application execution and checkpointing, from the lower layers of the framework. The focus is clearly on efficiency, ease of use and functionality. The interface has to be adapted to the original BFT-SMaRt single-threaded execution and to our own SMRteez multi-threaded execution. Functionality for tentative execution is currently not implemented in existing execution layers. Here this work is supposed to do performance tests of the API based on mock-up implementations.
“Trust Analysis of Traffic Sign Classifiers under Occlusions,” Bachelor's thesis or Master's thesis, D. Eisermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2025 – Open.
This thesis aims to investigate the reliability and trustworthiness of traffic sign classifiers when subjected to occlusions. Utilizing the German Traffic Sign Recognition Benchmark (GTSRB) dataset, this research will focus on annotating the dataset with various levels and types of occlusions to evaluate if the predictions are still trustworthy. The primary objective is to assess the performance degradation of the classifier under different occlusion scenarios and to develop strategies to enhance its robustness. This study is crucial for improving the safety and reliability of autonomous driving systems where traffic signs might be partially obscured.
“Evaluation of AI-Based and Non-AI-Based Misbehavior Detection Systems,” Project or Bachelor's thesis or Master's thesis, A. Hermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2025 – Open.
The growing connectivity of cooperative intelligent transportation systems (C-ITS) raises critical security concerns, particularly regarding the trustworthiness of exchanged information. Misbehavior Detection Systems (MDS) play a key role in identifying malicious or faulty behavior in vehicular networks. This thesis evaluates both AI-based and non-AI-based MDS approaches, comparing their detection performance and computational efficiency under different attack scenarios. Using a structured benchmarking framework and representative vehicular datasets, the study analyzes different kinds of MBDs.
“Evaluation of Intrusion Detection Systems in In-Vehicle Networks,” Project or Bachelor's thesis or Master's thesis, A. Hermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2025 – Open.
Modern vehicles rely on complex in-vehicle networks to support safety-critical and comfort functions. As these networks become more interconnected, they face increasing security risks from malicious attacks and faulty components. Intrusion Detection Systems (IDS) are essential for detecting abnormal behavior and protecting the integrity of in-vehicle communication. This thesis evaluates different IDS approaches for automotive networks, including rule-based and machine learning methods. Using representative datasets and realistic attack scenarios, the study compares detection accuracy, false positive rates, and computational efficiency
“Detection of Natural Adversarial Examples against ImageNet Classifiers,” Master's thesis, D. Eisermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2025 – Open.
This thesis will investigate methods for detecting natural adversarial examples against ImageNet classifiers using classic computer vision techniques. Adversarial examples are inputs to machine learning models that are designed to cause the model to make a mistake. This project will utilize the Harder ImageNet Test Set (https://arxiv.org/abs/1907.07174) as an dataset for Natural Adversarial Examples. The primary objective is to explore and compare the effectiveness of traditional computer vision methods, such as histograms and SIFT (Scale-Invariant Feature Transform), in identifying these adversarial examples. The outcome of this research will enhance our understanding of model vulnerabilities and contribute to developing more robust machine learning systems.
“A Comparison of Various Optimization Strategies for Generating Adversarial Patches,” Bachelor's thesis or Master's thesis, D. Eisermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2025 – Open.
This thesis will explore the effectiveness of different optimization strategies in the generation of adversarial patches. Adversarial patches are small, intentionally designed perturbations that can cause machine learning models, particularly in computer vision, to misclassify inputs. The primary objective of this research is to compare various optimization techniques, such as gradient-based methods, evolutionary algorithms, and reinforcement learning, to determine which methods are most effective and efficient in creating these patches. The outcome of this research could significantly enhance our understanding of model vulnerabilities and contribute to the development of more robust machine learning systems.
“V2X Communication for Mount Bike Applications,” B.Sc. / M.Sc. Thesis or Project, F. Kargl (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2024 – Open.
The alps see a surge of mountain biking as a recreational activity. This leads to frequent encounters of hikers and bikers on shared trails, but also to crashes between bikers due to, bad visibility in curves. In our previous work, we have investigated various scenarios and solutions, for example, a biker-to-hiker warning system, or a collision warning system for bike parks. Essential elements for these systems include localization of bikers in alpine environments, communication with near-range radio technologies like WiFi or BLE, but also suitable design of user interfaces and many more. Based on such earlier works (documented in theses and publications), we already identified various open challenges and possible future work that you can contribute to through a thesis or project. Please contact us to identify and define a suitable topic definition fitting your interests and previous experience. The overall project is collaboration between Ulm University and University of Trento.
“Trust Analysis of Traffic Sign Classifiers under Occlusions,” Bachelor's thesis or Master's thesis, D. Eisermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2024 – Open.
This thesis aims to investigate the reliability and trustworthiness of traffic sign classifiers when subjected to occlusions. Utilizing the German Traffic Sign Recognition Benchmark (GTSRB) dataset, this research will focus on annotating the dataset with various levels and types of occlusions to evaluate if the predictions are still trustworthy. The primary objective is to assess the performance degradation of the classifier under different occlusion scenarios and to develop strategies to enhance its robustness. This study is crucial for improving the safety and reliability of autonomous driving systems where traffic signs might be partially obscured.
“Detection of Natural Adversarial Examples against ImageNet Classifiers,” Master's thesis, D. Eisermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2024 – Open.
This thesis will investigate methods for detecting natural adversarial examples against ImageNet classifiers using classic computer vision techniques. Adversarial examples are inputs to machine learning models that are designed to cause the model to make a mistake. This project will utilize the Harder ImageNet Test Set (https://arxiv.org/abs/1907.07174) as an dataset for Natural Adversarial Examples. The primary objective is to explore and compare the effectiveness of traditional computer vision methods, such as histograms and SIFT (Scale-Invariant Feature Transform), in identifying these adversarial examples. The outcome of this research will enhance our understanding of model vulnerabilities and contribute to developing more robust machine learning systems.
“Automating Trust Modeling Based On Vehicular System Models,” Bachelor or Master's thesis, N. Trkulja (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2024 – Open.
An autonomous vehicle is equipped with a variety of sensors that produce large quantites of data which the vehicle uses to run a lot of different safety-critical functions, such as Cooperative Adaptive Cruise Control or Park Assist. In this thesis, we focus on the trust between the vehicle computer and other in-vehicle components that it relies upon to provide non-compromised data as input to different safety-critical functions. The goal of the thesis is to build a tool that will automate building of in-vehicular trust models based on a system model of a vehicle. A system model of a simplified vehicle will first need to be created by using the System Modeling Language (SysML). This model will serve as an input to the automation tool that needs to output a trust model in a pre-defined form. The methodology for building such trust models will be provided.
“A Comparison of Various Optimization Strategies for Generating Adversarial Patches,” Bachelor's thesis or Master's thesis, D. Eisermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2024 – Open.
This thesis will explore the effectiveness of different optimization strategies in the generation of adversarial patches. Adversarial patches are small, intentionally designed perturbations that can cause machine learning models, particularly in computer vision, to misclassify inputs. The primary objective of this research is to compare various optimization techniques, such as gradient-based methods, evolutionary algorithms, and reinforcement learning, to determine which methods are most effective and efficient in creating these patches. The outcome of this research could significantly enhance our understanding of model vulnerabilities and contribute to the development of more robust machine learning systems.
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Jessica Reib
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Phone: +49 731 50-24140
Fax: +49 731 50-24142

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Institute of Distributed Systems
Ulm University
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89081 Ulm

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Building O27, Room 349
89081 Ulm

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Friday 8am to 2pm

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Topics By Degree

Topics for Bachelor Theses

“Re-Implementing Zookeeper using an SMR framework,” Bachelor Thesis, Project, F. J. Hauck (Supervisor), F. J. Hauck (Examiner), Inst. of Distr. Sys., Ulm Univ., 2026 – Open.
State-machine replication is a concept to achieve fault tolerance. There are several frameworks to support SMR-based applications. Zookeeper is an application implementing a so-called coordination service. It is internally build with SMR technology, but does not use an underlying framework. The task of this work is to reimplement Zookeeper with the BFT-SMaRt/SMRteez framework. The goal is to demonstrate that the framework can handle such applications. In case of a Bachelor's thesis, in case of remaining time also in case of project work, the performance shall be compared to the original Zookeeper implementation. For the implementation it can be expected that at least some code can be reused from the original implementation.
“Trust Analysis of Traffic Sign Classifiers under Occlusions,” Bachelor's thesis or Master's thesis, D. Eisermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2025 – Open.
This thesis aims to investigate the reliability and trustworthiness of traffic sign classifiers when subjected to occlusions. Utilizing the German Traffic Sign Recognition Benchmark (GTSRB) dataset, this research will focus on annotating the dataset with various levels and types of occlusions to evaluate if the predictions are still trustworthy. The primary objective is to assess the performance degradation of the classifier under different occlusion scenarios and to develop strategies to enhance its robustness. This study is crucial for improving the safety and reliability of autonomous driving systems where traffic signs might be partially obscured.
“Evaluation of AI-Based and Non-AI-Based Misbehavior Detection Systems,” Project or Bachelor's thesis or Master's thesis, A. Hermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2025 – Open.
The growing connectivity of cooperative intelligent transportation systems (C-ITS) raises critical security concerns, particularly regarding the trustworthiness of exchanged information. Misbehavior Detection Systems (MDS) play a key role in identifying malicious or faulty behavior in vehicular networks. This thesis evaluates both AI-based and non-AI-based MDS approaches, comparing their detection performance and computational efficiency under different attack scenarios. Using a structured benchmarking framework and representative vehicular datasets, the study analyzes different kinds of MBDs.
“Evaluation of Intrusion Detection Systems in In-Vehicle Networks,” Project or Bachelor's thesis or Master's thesis, A. Hermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2025 – Open.
Modern vehicles rely on complex in-vehicle networks to support safety-critical and comfort functions. As these networks become more interconnected, they face increasing security risks from malicious attacks and faulty components. Intrusion Detection Systems (IDS) are essential for detecting abnormal behavior and protecting the integrity of in-vehicle communication. This thesis evaluates different IDS approaches for automotive networks, including rule-based and machine learning methods. Using representative datasets and realistic attack scenarios, the study compares detection accuracy, false positive rates, and computational efficiency
“A Comparison of Various Optimization Strategies for Generating Adversarial Patches,” Bachelor's thesis or Master's thesis, D. Eisermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2025 – Open.
This thesis will explore the effectiveness of different optimization strategies in the generation of adversarial patches. Adversarial patches are small, intentionally designed perturbations that can cause machine learning models, particularly in computer vision, to misclassify inputs. The primary objective of this research is to compare various optimization techniques, such as gradient-based methods, evolutionary algorithms, and reinforcement learning, to determine which methods are most effective and efficient in creating these patches. The outcome of this research could significantly enhance our understanding of model vulnerabilities and contribute to the development of more robust machine learning systems.
“V2X Communication for Mount Bike Applications,” B.Sc. / M.Sc. Thesis or Project, F. Kargl (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2024 – Open.
The alps see a surge of mountain biking as a recreational activity. This leads to frequent encounters of hikers and bikers on shared trails, but also to crashes between bikers due to, bad visibility in curves. In our previous work, we have investigated various scenarios and solutions, for example, a biker-to-hiker warning system, or a collision warning system for bike parks. Essential elements for these systems include localization of bikers in alpine environments, communication with near-range radio technologies like WiFi or BLE, but also suitable design of user interfaces and many more. Based on such earlier works (documented in theses and publications), we already identified various open challenges and possible future work that you can contribute to through a thesis or project. Please contact us to identify and define a suitable topic definition fitting your interests and previous experience. The overall project is collaboration between Ulm University and University of Trento.
“Trust Analysis of Traffic Sign Classifiers under Occlusions,” Bachelor's thesis or Master's thesis, D. Eisermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2024 – Open.
This thesis aims to investigate the reliability and trustworthiness of traffic sign classifiers when subjected to occlusions. Utilizing the German Traffic Sign Recognition Benchmark (GTSRB) dataset, this research will focus on annotating the dataset with various levels and types of occlusions to evaluate if the predictions are still trustworthy. The primary objective is to assess the performance degradation of the classifier under different occlusion scenarios and to develop strategies to enhance its robustness. This study is crucial for improving the safety and reliability of autonomous driving systems where traffic signs might be partially obscured.
“Automating Trust Modeling Based On Vehicular System Models,” Bachelor or Master's thesis, N. Trkulja (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2024 – Open.
An autonomous vehicle is equipped with a variety of sensors that produce large quantites of data which the vehicle uses to run a lot of different safety-critical functions, such as Cooperative Adaptive Cruise Control or Park Assist. In this thesis, we focus on the trust between the vehicle computer and other in-vehicle components that it relies upon to provide non-compromised data as input to different safety-critical functions. The goal of the thesis is to build a tool that will automate building of in-vehicular trust models based on a system model of a vehicle. A system model of a simplified vehicle will first need to be created by using the System Modeling Language (SysML). This model will serve as an input to the automation tool that needs to output a trust model in a pre-defined form. The methodology for building such trust models will be provided.
“A Comparison of Various Optimization Strategies for Generating Adversarial Patches,” Bachelor's thesis or Master's thesis, D. Eisermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2024 – Open.
This thesis will explore the effectiveness of different optimization strategies in the generation of adversarial patches. Adversarial patches are small, intentionally designed perturbations that can cause machine learning models, particularly in computer vision, to misclassify inputs. The primary objective of this research is to compare various optimization techniques, such as gradient-based methods, evolutionary algorithms, and reinforcement learning, to determine which methods are most effective and efficient in creating these patches. The outcome of this research could significantly enhance our understanding of model vulnerabilities and contribute to the development of more robust machine learning systems.

Topics for Master Theses

“Development of an execution-layer interface for an SMR framework,” Master's thesis, F. J. Hauck (Supervisor), F. J. Hauck (Examiner), Inst. of Distr. Sys., Ulm Univ., 2026 – Open.
State-machine replication is a concept to achieve fault tolerance. There are several frameworks to support SMR-based applications including BFT-SMaRt/SMRteez. In this framework the interface to the layer that actually executes the application is somewhat tangled due to its history and due to bad abstractions. The goal of this work is to develop a modern and efficient API to separate the execution layer, responsible for application execution and checkpointing, from the lower layers of the framework. The focus is clearly on efficiency, ease of use and functionality. The interface has to be adapted to the original BFT-SMaRt single-threaded execution and to our own SMRteez multi-threaded execution. Functionality for tentative execution is currently not implemented in existing execution layers. Here this work is supposed to do performance tests of the API based on mock-up implementations.
“Trust Analysis of Traffic Sign Classifiers under Occlusions,” Bachelor's thesis or Master's thesis, D. Eisermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2025 – Open.
This thesis aims to investigate the reliability and trustworthiness of traffic sign classifiers when subjected to occlusions. Utilizing the German Traffic Sign Recognition Benchmark (GTSRB) dataset, this research will focus on annotating the dataset with various levels and types of occlusions to evaluate if the predictions are still trustworthy. The primary objective is to assess the performance degradation of the classifier under different occlusion scenarios and to develop strategies to enhance its robustness. This study is crucial for improving the safety and reliability of autonomous driving systems where traffic signs might be partially obscured.
“Evaluation of AI-Based and Non-AI-Based Misbehavior Detection Systems,” Project or Bachelor's thesis or Master's thesis, A. Hermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2025 – Open.
The growing connectivity of cooperative intelligent transportation systems (C-ITS) raises critical security concerns, particularly regarding the trustworthiness of exchanged information. Misbehavior Detection Systems (MDS) play a key role in identifying malicious or faulty behavior in vehicular networks. This thesis evaluates both AI-based and non-AI-based MDS approaches, comparing their detection performance and computational efficiency under different attack scenarios. Using a structured benchmarking framework and representative vehicular datasets, the study analyzes different kinds of MBDs.
“Evaluation of Intrusion Detection Systems in In-Vehicle Networks,” Project or Bachelor's thesis or Master's thesis, A. Hermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2025 – Open.
Modern vehicles rely on complex in-vehicle networks to support safety-critical and comfort functions. As these networks become more interconnected, they face increasing security risks from malicious attacks and faulty components. Intrusion Detection Systems (IDS) are essential for detecting abnormal behavior and protecting the integrity of in-vehicle communication. This thesis evaluates different IDS approaches for automotive networks, including rule-based and machine learning methods. Using representative datasets and realistic attack scenarios, the study compares detection accuracy, false positive rates, and computational efficiency
“Detection of Natural Adversarial Examples against ImageNet Classifiers,” Master's thesis, D. Eisermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2025 – Open.
This thesis will investigate methods for detecting natural adversarial examples against ImageNet classifiers using classic computer vision techniques. Adversarial examples are inputs to machine learning models that are designed to cause the model to make a mistake. This project will utilize the Harder ImageNet Test Set (https://arxiv.org/abs/1907.07174) as an dataset for Natural Adversarial Examples. The primary objective is to explore and compare the effectiveness of traditional computer vision methods, such as histograms and SIFT (Scale-Invariant Feature Transform), in identifying these adversarial examples. The outcome of this research will enhance our understanding of model vulnerabilities and contribute to developing more robust machine learning systems.
“A Comparison of Various Optimization Strategies for Generating Adversarial Patches,” Bachelor's thesis or Master's thesis, D. Eisermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2025 – Open.
This thesis will explore the effectiveness of different optimization strategies in the generation of adversarial patches. Adversarial patches are small, intentionally designed perturbations that can cause machine learning models, particularly in computer vision, to misclassify inputs. The primary objective of this research is to compare various optimization techniques, such as gradient-based methods, evolutionary algorithms, and reinforcement learning, to determine which methods are most effective and efficient in creating these patches. The outcome of this research could significantly enhance our understanding of model vulnerabilities and contribute to the development of more robust machine learning systems.
“V2X Communication for Mount Bike Applications,” B.Sc. / M.Sc. Thesis or Project, F. Kargl (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2024 – Open.
The alps see a surge of mountain biking as a recreational activity. This leads to frequent encounters of hikers and bikers on shared trails, but also to crashes between bikers due to, bad visibility in curves. In our previous work, we have investigated various scenarios and solutions, for example, a biker-to-hiker warning system, or a collision warning system for bike parks. Essential elements for these systems include localization of bikers in alpine environments, communication with near-range radio technologies like WiFi or BLE, but also suitable design of user interfaces and many more. Based on such earlier works (documented in theses and publications), we already identified various open challenges and possible future work that you can contribute to through a thesis or project. Please contact us to identify and define a suitable topic definition fitting your interests and previous experience. The overall project is collaboration between Ulm University and University of Trento.
“Trust Analysis of Traffic Sign Classifiers under Occlusions,” Bachelor's thesis or Master's thesis, D. Eisermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2024 – Open.
This thesis aims to investigate the reliability and trustworthiness of traffic sign classifiers when subjected to occlusions. Utilizing the German Traffic Sign Recognition Benchmark (GTSRB) dataset, this research will focus on annotating the dataset with various levels and types of occlusions to evaluate if the predictions are still trustworthy. The primary objective is to assess the performance degradation of the classifier under different occlusion scenarios and to develop strategies to enhance its robustness. This study is crucial for improving the safety and reliability of autonomous driving systems where traffic signs might be partially obscured.
“Detection of Natural Adversarial Examples against ImageNet Classifiers,” Master's thesis, D. Eisermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2024 – Open.
This thesis will investigate methods for detecting natural adversarial examples against ImageNet classifiers using classic computer vision techniques. Adversarial examples are inputs to machine learning models that are designed to cause the model to make a mistake. This project will utilize the Harder ImageNet Test Set (https://arxiv.org/abs/1907.07174) as an dataset for Natural Adversarial Examples. The primary objective is to explore and compare the effectiveness of traditional computer vision methods, such as histograms and SIFT (Scale-Invariant Feature Transform), in identifying these adversarial examples. The outcome of this research will enhance our understanding of model vulnerabilities and contribute to developing more robust machine learning systems.
“Automating Trust Modeling Based On Vehicular System Models,” Bachelor or Master's thesis, N. Trkulja (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2024 – Open.
An autonomous vehicle is equipped with a variety of sensors that produce large quantites of data which the vehicle uses to run a lot of different safety-critical functions, such as Cooperative Adaptive Cruise Control or Park Assist. In this thesis, we focus on the trust between the vehicle computer and other in-vehicle components that it relies upon to provide non-compromised data as input to different safety-critical functions. The goal of the thesis is to build a tool that will automate building of in-vehicular trust models based on a system model of a vehicle. A system model of a simplified vehicle will first need to be created by using the System Modeling Language (SysML). This model will serve as an input to the automation tool that needs to output a trust model in a pre-defined form. The methodology for building such trust models will be provided.
“A Comparison of Various Optimization Strategies for Generating Adversarial Patches,” Bachelor's thesis or Master's thesis, D. Eisermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2024 – Open.
This thesis will explore the effectiveness of different optimization strategies in the generation of adversarial patches. Adversarial patches are small, intentionally designed perturbations that can cause machine learning models, particularly in computer vision, to misclassify inputs. The primary objective of this research is to compare various optimization techniques, such as gradient-based methods, evolutionary algorithms, and reinforcement learning, to determine which methods are most effective and efficient in creating these patches. The outcome of this research could significantly enhance our understanding of model vulnerabilities and contribute to the development of more robust machine learning systems.

Topics By Research Area

Cloud Computing

Distributed Computing & Data-intensive Systems

“Re-Implementing Zookeeper using an SMR framework,” Bachelor Thesis, Project, F. J. Hauck (Supervisor), F. J. Hauck (Examiner), Inst. of Distr. Sys., Ulm Univ., 2026 – Open.
State-machine replication is a concept to achieve fault tolerance. There are several frameworks to support SMR-based applications. Zookeeper is an application implementing a so-called coordination service. It is internally build with SMR technology, but does not use an underlying framework. The task of this work is to reimplement Zookeeper with the BFT-SMaRt/SMRteez framework. The goal is to demonstrate that the framework can handle such applications. In case of a Bachelor's thesis, in case of remaining time also in case of project work, the performance shall be compared to the original Zookeeper implementation. For the implementation it can be expected that at least some code can be reused from the original implementation.
“Development of an execution-layer interface for an SMR framework,” Master's thesis, F. J. Hauck (Supervisor), F. J. Hauck (Examiner), Inst. of Distr. Sys., Ulm Univ., 2026 – Open.
State-machine replication is a concept to achieve fault tolerance. There are several frameworks to support SMR-based applications including BFT-SMaRt/SMRteez. In this framework the interface to the layer that actually executes the application is somewhat tangled due to its history and due to bad abstractions. The goal of this work is to develop a modern and efficient API to separate the execution layer, responsible for application execution and checkpointing, from the lower layers of the framework. The focus is clearly on efficiency, ease of use and functionality. The interface has to be adapted to the original BFT-SMaRt single-threaded execution and to our own SMRteez multi-threaded execution. Functionality for tentative execution is currently not implemented in existing execution layers. Here this work is supposed to do performance tests of the API based on mock-up implementations.

Fault Tolerance

“Re-Implementing Zookeeper using an SMR framework,” Bachelor Thesis, Project, F. J. Hauck (Supervisor), F. J. Hauck (Examiner), Inst. of Distr. Sys., Ulm Univ., 2026 – Open.
State-machine replication is a concept to achieve fault tolerance. There are several frameworks to support SMR-based applications. Zookeeper is an application implementing a so-called coordination service. It is internally build with SMR technology, but does not use an underlying framework. The task of this work is to reimplement Zookeeper with the BFT-SMaRt/SMRteez framework. The goal is to demonstrate that the framework can handle such applications. In case of a Bachelor's thesis, in case of remaining time also in case of project work, the performance shall be compared to the original Zookeeper implementation. For the implementation it can be expected that at least some code can be reused from the original implementation.
“Development of an execution-layer interface for an SMR framework,” Master's thesis, F. J. Hauck (Supervisor), F. J. Hauck (Examiner), Inst. of Distr. Sys., Ulm Univ., 2026 – Open.
State-machine replication is a concept to achieve fault tolerance. There are several frameworks to support SMR-based applications including BFT-SMaRt/SMRteez. In this framework the interface to the layer that actually executes the application is somewhat tangled due to its history and due to bad abstractions. The goal of this work is to develop a modern and efficient API to separate the execution layer, responsible for application execution and checkpointing, from the lower layers of the framework. The focus is clearly on efficiency, ease of use and functionality. The interface has to be adapted to the original BFT-SMaRt single-threaded execution and to our own SMRteez multi-threaded execution. Functionality for tentative execution is currently not implemented in existing execution layers. Here this work is supposed to do performance tests of the API based on mock-up implementations.

IT Security

“Trust Analysis of Traffic Sign Classifiers under Occlusions,” Bachelor's thesis or Master's thesis, D. Eisermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2025 – Open.
This thesis aims to investigate the reliability and trustworthiness of traffic sign classifiers when subjected to occlusions. Utilizing the German Traffic Sign Recognition Benchmark (GTSRB) dataset, this research will focus on annotating the dataset with various levels and types of occlusions to evaluate if the predictions are still trustworthy. The primary objective is to assess the performance degradation of the classifier under different occlusion scenarios and to develop strategies to enhance its robustness. This study is crucial for improving the safety and reliability of autonomous driving systems where traffic signs might be partially obscured.
“Evaluation of AI-Based and Non-AI-Based Misbehavior Detection Systems,” Project or Bachelor's thesis or Master's thesis, A. Hermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2025 – Open.
The growing connectivity of cooperative intelligent transportation systems (C-ITS) raises critical security concerns, particularly regarding the trustworthiness of exchanged information. Misbehavior Detection Systems (MDS) play a key role in identifying malicious or faulty behavior in vehicular networks. This thesis evaluates both AI-based and non-AI-based MDS approaches, comparing their detection performance and computational efficiency under different attack scenarios. Using a structured benchmarking framework and representative vehicular datasets, the study analyzes different kinds of MBDs.
“Evaluation of Intrusion Detection Systems in In-Vehicle Networks,” Project or Bachelor's thesis or Master's thesis, A. Hermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2025 – Open.
Modern vehicles rely on complex in-vehicle networks to support safety-critical and comfort functions. As these networks become more interconnected, they face increasing security risks from malicious attacks and faulty components. Intrusion Detection Systems (IDS) are essential for detecting abnormal behavior and protecting the integrity of in-vehicle communication. This thesis evaluates different IDS approaches for automotive networks, including rule-based and machine learning methods. Using representative datasets and realistic attack scenarios, the study compares detection accuracy, false positive rates, and computational efficiency
“Detection of Natural Adversarial Examples against ImageNet Classifiers,” Master's thesis, D. Eisermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2025 – Open.
This thesis will investigate methods for detecting natural adversarial examples against ImageNet classifiers using classic computer vision techniques. Adversarial examples are inputs to machine learning models that are designed to cause the model to make a mistake. This project will utilize the Harder ImageNet Test Set (https://arxiv.org/abs/1907.07174) as an dataset for Natural Adversarial Examples. The primary objective is to explore and compare the effectiveness of traditional computer vision methods, such as histograms and SIFT (Scale-Invariant Feature Transform), in identifying these adversarial examples. The outcome of this research will enhance our understanding of model vulnerabilities and contribute to developing more robust machine learning systems.
“A Comparison of Various Optimization Strategies for Generating Adversarial Patches,” Bachelor's thesis or Master's thesis, D. Eisermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2025 – Open.
This thesis will explore the effectiveness of different optimization strategies in the generation of adversarial patches. Adversarial patches are small, intentionally designed perturbations that can cause machine learning models, particularly in computer vision, to misclassify inputs. The primary objective of this research is to compare various optimization techniques, such as gradient-based methods, evolutionary algorithms, and reinforcement learning, to determine which methods are most effective and efficient in creating these patches. The outcome of this research could significantly enhance our understanding of model vulnerabilities and contribute to the development of more robust machine learning systems.
“Trust Analysis of Traffic Sign Classifiers under Occlusions,” Bachelor's thesis or Master's thesis, D. Eisermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2024 – Open.
This thesis aims to investigate the reliability and trustworthiness of traffic sign classifiers when subjected to occlusions. Utilizing the German Traffic Sign Recognition Benchmark (GTSRB) dataset, this research will focus on annotating the dataset with various levels and types of occlusions to evaluate if the predictions are still trustworthy. The primary objective is to assess the performance degradation of the classifier under different occlusion scenarios and to develop strategies to enhance its robustness. This study is crucial for improving the safety and reliability of autonomous driving systems where traffic signs might be partially obscured.
“Detection of Natural Adversarial Examples against ImageNet Classifiers,” Master's thesis, D. Eisermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2024 – Open.
This thesis will investigate methods for detecting natural adversarial examples against ImageNet classifiers using classic computer vision techniques. Adversarial examples are inputs to machine learning models that are designed to cause the model to make a mistake. This project will utilize the Harder ImageNet Test Set (https://arxiv.org/abs/1907.07174) as an dataset for Natural Adversarial Examples. The primary objective is to explore and compare the effectiveness of traditional computer vision methods, such as histograms and SIFT (Scale-Invariant Feature Transform), in identifying these adversarial examples. The outcome of this research will enhance our understanding of model vulnerabilities and contribute to developing more robust machine learning systems.
“Automating Trust Modeling Based On Vehicular System Models,” Bachelor or Master's thesis, N. Trkulja (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2024 – Open.
An autonomous vehicle is equipped with a variety of sensors that produce large quantites of data which the vehicle uses to run a lot of different safety-critical functions, such as Cooperative Adaptive Cruise Control or Park Assist. In this thesis, we focus on the trust between the vehicle computer and other in-vehicle components that it relies upon to provide non-compromised data as input to different safety-critical functions. The goal of the thesis is to build a tool that will automate building of in-vehicular trust models based on a system model of a vehicle. A system model of a simplified vehicle will first need to be created by using the System Modeling Language (SysML). This model will serve as an input to the automation tool that needs to output a trust model in a pre-defined form. The methodology for building such trust models will be provided.
“A Comparison of Various Optimization Strategies for Generating Adversarial Patches,” Bachelor's thesis or Master's thesis, D. Eisermann (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2024 – Open.
This thesis will explore the effectiveness of different optimization strategies in the generation of adversarial patches. Adversarial patches are small, intentionally designed perturbations that can cause machine learning models, particularly in computer vision, to misclassify inputs. The primary objective of this research is to compare various optimization techniques, such as gradient-based methods, evolutionary algorithms, and reinforcement learning, to determine which methods are most effective and efficient in creating these patches. The outcome of this research could significantly enhance our understanding of model vulnerabilities and contribute to the development of more robust machine learning systems.

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