With the rise of new space, space missions are becoming increasingly more accessible. This is caused by the increased use of commercial-off-the-shelf components as well as the possibility of having multiple parties operating on a single satellite platform. This development combined with a new attitude towards the security of these systems has exposed some flaws in current designs. These concerns are in the lack of defense-in-depth measures on board the satellite and the fully trusted nature of the internal bus. In this thesis we perform a high-level risk analysis for CubeSat missions and map them to the SPACE-SHIELD framework. We then implement several mitigations based on the identified risks, with a focus on less explored research areas. The performance of the mitigations is then evaluated in order to test their viability for use in the industry. We provide a simulator setup for testing and evaluating the mitigations. In order to improve the security of CubeSats we ran several fuzzing campaigns, which have led to the discovery of potentially vulnerable sections of code. Furthermore, we implemented mitigations to achieve network segmentation and end-to-end security on the internal bus of the device. The authenticated encryption scheme implemented for end-to-end security uses the NIST standard for lightweight cryptography known as Ascon. The measurements show that our reference implementation of this AEAD scheme has an overhead of 15% for message payload sizes of 200 bytes. Lastly, we contribute to several entries in the SPACE-SHIELD framework which were lacking before.

Federated learning (FL), although a major privacy improvement over centralized learning, is still vulnerable to privacy leaks. The research presented in this paper provides an analysis of the threats to FL Generative Adversarial Networks. Furthermore, an implementation is provided to better protect the data of the participants with Trusted Execution Environments (TEEs), using Intel Software Guard Extensions. Lastly, the viability of it’s use in practice is evaluated and discussed. The results indicate that this approach protects the data, while not affecting the predicting capabilities of the model, with a noticeable but manageable impact on the training duration.