Satellites have become part of critical infrastructure utilized for diverse applications, from Earth observation to communication and military missions. Several trends have reshaped satellite deployment and utilization in recent years, making satellite systems more accessible and vulnerable to cybersecurity threats. A notable trend is adopting Commercially Off-the-Shelf (COTS) hardware and software for satellite systems. However, this approach renders satellites susceptible to well-known cyberattacks. This paper presents a comprehensive exploration of attacks on satellite systems, with a specific emphasis on the security aspects of the satellite platform, encompassing both the bus and payload subsystems. The discussion includes existing security defenses that can enhance the security of the satellite platform. Ultimately, we present a real-world security framework designed to improve the overall security of the satellite platform.

Federated Learning (FL) has become very popular since it enables clients to train a joint model collaboratively without sharing their private data. However, FL has been shown to be susceptible to backdoor and inference attacks. While in the former, the adversary injects manipulated updates into the aggregation process; the latter leverages clients' local models to deduce their private data. Contemporary solutions to address the security concerns of FL are either impractical for real-world deployment due to high-performance overheads or are tailored towards addressing specific threats, for instance, privacy-preserving aggregation or backdoor defenses. Given these limitations, our research delves into the advantages of harnessing the FPGA-based computing paradigm to overcome performance bottlenecks of software-only solutions while mitigating backdoor and inference attacks. We utilize FPGA-based enclaves to address inference attacks during the aggregation process of FL. We adopt an advanced backdoor-aware aggregation algorithm on the FPGA to counter backdoor attacks. We implemented and evaluated our method on Xilinx VMK-180, yielding a significant speed-up of around 300 times on the IoT-Traffic dataset and more than 506 times on the CIFAR-10 dataset.