Security mechanisms in Vehicle-to-Everything (V2X) environments, such as misbehavior detection systems, generate outputs that indicate potential malicious behavior but do not directly provide a unified and interpretable trust representation. This thesis investigates methods for quantifying such outputs into subjective logic opinions that can be used by trust assessment frameworks. The focus lies on a machine learning–based approach that learns the mapping from security mechanism outputs to belief, disbelief, and uncertainty values. The proposed method will be compared against existing quantification techniques to evaluate improvements in accuracy, robustness, and interpretability. The evaluation will be conducted using realistic V2X datasets and scenarios.

Intel SGX is a technology that allows to launch tamper-proof enclaves in main memory, which isolate parts of applications that deal with sensitive data. There is a broad spectrum of application scenarios, ranging from fault-tolerant systems to privacy-preserving machine learning approaches. Intel provides a native SDK that can be used to derive low-level wrapper functions from a function definitions provided in a DSL, which are then used to interact with the protected parts of the application applications. However, the SDK requires special care during the design process as well as C/C++ programming skills, in order to create a bulletproof interface to the enclave. The Gramine project promises to simplify the SGX application development process by providing functionality to wrap unmodified linux applications in Intel SGX enclaves. Since this approach trades in performance for usability, the goal of this project is to conduct a performance evaluation for different applications launched natively and wrapped with Gramine.

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 project will focus on improving the trustworthiness of generated information through the fine-tuning of the Llama 3 8b model using the Unsloth training performance optimization library. The primary goal is to enhance the reliability and accuracy of AI-generated content by leveraging advanced training techniques. The research will involve evaluating the performance of the Llama 3 8b model before and after fine-tuning, analyzing improvements in trustworthiness metrics, and developing new methodologies to further optimize the model’s performance.

This project will investigate the performance differences between Kolmogorov-Arnold Networks (KANs) and Multi-Layer Perceptrons (MLPs) in the context of image classification tasks. Kolmogorov-Arnold Networks offer a novel approach to neural network architecture based on mathematical foundations that differ from traditional MLPs. The primary goal of this research is to empirically compare these two types of neural networks to evaluate their classification accuracy. The outcome of this research may provide insights into the potential advantages of KANs over conventional MLPs in practical applications.

This project will focus on improving the trustworthiness of generated information through the fine-tuning of the Llama 3 8b model using the Unsloth training performance optimization library. The primary goal is to enhance the reliability and accuracy of AI-generated content by leveraging advanced training techniques. The research will involve evaluating the performance of the Llama 3 8b model before and after fine-tuning, analyzing improvements in trustworthiness metrics, and developing new methodologies to further optimize the model’s performance.

This project will investigate the performance differences between Kolmogorov-Arnold Networks (KANs) and Multi-Layer Perceptrons (MLPs) in the context of image classification tasks. Kolmogorov-Arnold Networks offer a novel approach to neural network architecture based on mathematical foundations that differ from traditional MLPs. The primary goal of this research is to empirically compare these two types of neural networks to evaluate their classification accuracy. The outcome of this research may provide insights into the potential advantages of KANs over conventional MLPs in practical applications.