FAST: A Frequency-Aware Skewed Merkle Tree for FPGA-Secured Embedded Systems
Yu Zou, Mingjie Lin · 2019
Protection of external memory is important when an attacker could get physical accesses to the external memory bus. Compared to general-purpose systems, embedded systems are more vulnerable to physical attacks due to the portability. One of the attacks is a replay attack, which an attacker records data sent over a memory bus and replays it to pretend to be an authorized user. Traditionally, the replay attack is protected using a full, balanced Merkle Tree. Focusing on average-case performance and general-purpose systems, traversal and verification of Merkle Tree incur a huge latency overhead to each memory access. In contrast to general-purpose systems, embedded systems are normally application-specific, and program behaviors and memory access patterns are deterministic. Besides that, we also observed that not all memory locations are accessed equally frequently given a program. Based on these two observations, we propose FAST, a Frequency-Aware Skewed merkle Tree for application-specific embedded systems. After profiling a program in a simulation environment without involving any replay attack protection, we get a memory access frequency distribution. Afterward, we design an automatic and systematic approach to generate an application-specific optimal skewed Merkle Tree accordingly. We propose an efficient hardware architecture to accelerate FAST on FPGA, and by experimenting on five real-world benchmarks, our skewed Merkle Tree implementation outperforms baseline which uses a full balanced Merkle Tree by up to 3 times.