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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.