Cross-Device Profiled Side-Channel Attacks using Meta-Transfer Learning

Abstract

Deep learning (DL) based profiling side channel analysis (SCA) pose a great threat to embedded devices. An adversary can break the target encryption engine through physical leakage of power or electromagnetic (EM) emanations collected from a profiling device. However, creating a successful DL based SCA model relies on a large amount of data. This presents a large barrier to those interested in applying DL for SCA. In this paper, we propose a novel attack mechanism that adopts meta-transfer learning to transfer DL networks among target devices by judiciously extracting information from a profiling device even using different side-channel sources. Supported by our method, a cross-device and/or cross-domain SCA attack becomes possible among different designs. In comparison to previous attack methodologies, we significantly reduce training costs and the number of traces $(\lt 3$ for power and $\lt 8$ for EM) required for SCA attacks on both unprotected or masked Advanced Encryption Standard (AES) implementations.