Attacking High-Performance SBCs: A Generic Preprocessing Framework for EMA
Debao Wang, Yiwen Gao, Jingdian Ming, Yongbin Zhou, Xian Feng Huang · 2024
For the high-performance single-board computers (SBCs) running an operating system, side-channel attacks usually come at high analytical costs, requiring millions of traces. This work uses the case of electromagnetic attacks on encryption services in real-world SBCs to explore how various preprocessing methods can reduce the attack costs for such devices. Specifically, we propose a general preprocessing framework that effectively combines multiple preprocessing methods based on their inherent characteristics. Utilizing this framework, we design a four-layer preprocessing scheme that significantly reduces the number of traces for key recovery. The experimental results show that, when the attack success rate reaches 80 percent, the proposed preprocessing scheme reduces the number of traces required by a factor of approximately 10 compared to the latest alignment algorithm on the Raspberry Pi 2B. Our research demonstrates the feasibility of conducting low-cost side-channel attacks on SBCs, further emphasizing the need to protect sensitive applications running on these devices.