Project Computer Networks and IT Security I

This thesis will take a quantitative look into statistical practices in empirical research. By analyzing the code that is submitted with preprints on the common preprint server PsyArXiv, we will identify common statistical pipelines and practices. This will allow us to gain insights into common patters and potential pitfalls, which can then aid future research in developing better tools and guidelines for statistical analysis.

GNSS spoofing poses a significant threat to systems relying on accurate positioning, particularly in safety-critical domains such as autonomous driving. However, the evaluation of spoofing detection methods is often limited by the lack of standardized benchmark datasets. This project addresses this gap by designing and implementing a benchmark dataset for GNSS spoofing detection systems. The dataset includes diverse and realistic spoofing scenarios with well-defined ground truth, enabling systematic and reproducible evaluation. The dataset is validated using existing GNSS spoofing detection approaches.

Preprint servers often host multiple versions of the same manuscript, but readers rarely learn what changed between them. In this thesis we will classify changes across preprint versions and distinguishing cosmetic edits (formatting, typography, ...) from substantive ones (altered results, revised claims, ...). Applied to roughly 5000 multi-version PsyArXiv preprints, the method quantifies how often version updates are substantive and how often such changes are disclosed in any author-provided note.