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.

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.

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

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.

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.

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.

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.