5G non-terrestrial networks (NTN) are getting increasing attention as a complementary solution to the currently deployed 5G terrestrial networks (TN) to provide global connectivity and ensure service continuity, service ubiquity, and service scalability. However, little research has been done into the security architecture of 5G NTN. This thesis aims to close this gap by summarizing the security architecture of 5G terrestrial networks and extending it to 5G non-terrestrial networks. In our security analysis, we are the first to perform a head-to-head comparison of four different NTN architectures (Transparent payload, Full gNB on board, Split CU-DU, and UE-Satellite-UE communication) with the first of its kind head-to-head comparison of the security architecture of 5G terrestrial and non-terrestrial networks.

In the practical part of the thesis, we implement a flooding attack against a 5G base station using OpenAirInterface (OAI), one of the largest open-source 5G network implementations, and evaluate the attack in a terrestrial and a non-terrestrial setup. In the performed experiments using real SDR devices (TN) and simulated LEO and GEO satellites with a transparent payload (NTN), we managed to make the base station permanently allocate more contexts than the defined threshold on the active connections, allowing an attacker to completely exhaust the available memory resources in the long run. Furthermore, we were able to reach the maximum number of allowed connections in the base station in all experiments except those with a GEO satellite, leading to a DoS of a legitimate subscriber. ... Read more

Rapid advancements in digital medical technologies have significantly improved patient care but have also raised complex security and privacy challenges. Traditional tools for detecting vulnerabilities in networked medical devices, primarily used by network administrators and security specialists, have become insufficient due to their large-scale use across the entire healthcare network. Aiming to improve security in healthcare, MedTech Chain proposes a way to solve this challenge by leveraging blockchain and privacy-enhancing technologies, offering an authenticated, decentralised, secure, and privacy-preserving environment for the research and monitoring of medical device data. Currently, the framework enables counting, averaging, and grouped counting queries with multiple filtering capabilities like time frame and location. Such functionalities can provide valuable insights not only for threat intelligence but also for medical research and hospital management. MedTech Chain is modular and flexible, designed to seamlessly extend to new device technologies and research demands. To our knowledge, the approach is among the first to employ ϵ-differential privacy in the context of medical device data. ... Read more

SAGE is an unsupervised sequence learning pipeline that generates alert-driven attack graphs (AGs) without the need for prior expert knowledge about existing vulnerabilities and network topology. Using a suffix-based probabilistic deterministic finite automaton (S-PDFA), it accentuates infrequent high-severity alerts without discarding frequent low-severity alerts. It also captures the context of the alerts with identical signatures and it is an interpretable model. In order to deal with infrequent data, SAGE utilises sink states which are not merged during the S-PDFA learning process. However, this could result in unnecessarily larger AGs. In this study, we have looked at the AGs resulting from merging sink states with other sinks and the core of the S-PDFA after the main merging process. Data from Collegiate Penetration Testing Competitions has been used to compare AGs based on the four metrics: size, complexity, interpretability and completeness. We have shown that the resulting graphs are, on average, slightly smaller, with about the same complexity and the same completeness, but with worse interpretability due to losses of context of attack episodes, which cannot be compensated by the slightly smaller size of the AGs. ... Read more