Microcontroller systems are integral to our daily lives, powering mission-critical applications such as vehicles, medical devices, and industrial control systems. Therefore, it is essential to investigate and outline the challenges encountered in developing secure microcontroller systems. While previous research has focused solely on microcontroller firmware analysis to identify and characterize vulnerabilities, our study uniquely leverages data from the 2023 and 2024 MITRE eCTF team submissions and post-competition interviews. This approach allows us to dissect the entire lifecycle of secure microcontroller system development from both technical and perceptual perspectives, providing deeper insights into how these vulnerabilities emerge in the first place.

Quantum Federated Learning (QFL) has emerged as a promising research direction by combining the strengths of quantum computing and federated learning. However, existing QFL solutions have consistently failed to simultaneously improve client training efficiency and ensure communication security. In this paper, we present a novel Multi-qubit Broadcast-based QFL framework (MB-QFL) to address the efficiency and security challenges of existing approaches. The framework employs a novel multi-qubit broadcast protocol and a quantum average method to secure the information transmission process. The multi-qubit broadcast protocol overcomes the limitations of existing protocols by allowing the transmission of an arbitraryS-qubit state from one sender to multiple (Q) receivers, whereas earlier protocols were restricted to broadcast one or two qubit state to recipients. Additionally, we propose an averaging method for quantum states, which exploits the probabilistic cloning technique to achieve aggregation in MB-QFL. The security analysis demonstrates that MB-QFL can effectively protect against inference attacks from malicious clients, as well as eavesdropping and intercept-and-resend attacks during communication. The algorithm complexity of MB-QFL is significantly lower than existing QFLs. Besides, the experimental results indicate that MB-QFL achieves higher classification accuracy than other QFLs.

Despite of their benefits, existing Trusted Execution Environments (TEE) or enclaves have been criticized for lack of transparency, vulnerabilities, and various restrictions. A significant limitation is that they only provide a static and fixed hardware Trusted Computing Base (TCB) that cannot be customized for different applications. The design violates the principle of least privilege by including unnecessary peripherals in the hardware TCB and buggy peripheral drivers in the software TCB. Additionally, Existing TEEs time-share a processor core with the Rich Execution Environment (REE), making execution less efficient and vulnerable to cache side-channel attacks. Although many previous projects have focused on addressing software issues in TEEs on SGX, TrustZone, or RISC-V, some TEE issues are inherent in the hardware system's design, making them impossible to resolve with software alone.