Portability of Profiling Side-Channel Analysis: A Case Study Using Adjustable Implementations of Block Ciphers

Inconsistencies in manufacturing features, sampling settings, and cryptographic implementations amongst the profiling and target devices can lead to the failure of profiling side-channel analysis (SCA). Various techniques, such as preprocessing, multi-device training, and transfer learning, have been proposed to mitigate this portability problem in profiling SCA. However, many techniques of block ciphers, such as tweaks, key-dependent components, and customized elements, might have uncertain effects from the perspective of cryptographic implementations, requiring further insightful analysis on their impact on portability. This paper investigates the portability of profiling SCA from a case study using adjustable implementations of block ciphers. First, we theoretically analyze the variation in leakage distribution under adjustable implementations. To support our theoretical results, a dataset of deep-learning SCA is built from AES, Pilsung, and Skinny. Specifically, we reveal how to reverse the parameterized components and recover the key from these adjustable implementations. According to our experiment on an 8-bit AVR microcontroller, the computational complexities of the attacks based on our model are less than 9 × 216within 4500 traces. Moreover, the effectiveness of our proposed method is demonstrated under the combinatorial effect with adjustable implementations and device characteristics. Our case study provides insights into the results of adjustable implementations of block ciphers, which strengthens both the theoretical and practical understanding of the portability of profiling SCA.