A Second Look at the Portability of Deep Learning Side-Channel Attacks over EM Traces

Deep learning side-channel attacks can recover encryption keys on a target by analyzing power consumption or electromagnetic (EM) signals. However, they are less portable when there are domain shifts between training and test data. While existing studies have shown that pre-processing and unsupervised domain adaptation can enhance the portability of deep learning side-channel attacks given domain shifts over EM traces, the findings are limited to easy targets (e.g. 8-bit microcontrollers). In this paper, we investigate the portability of deep learning side-channel attacks over EM traces acquired from more challenging targets, including 32-bit microcontrollers and EM traces with random delay. We study domain shifts introduced by the combination of hardware variations, distinct keys, and inconsistent probe locations between two targets. In addition, we perform comparative analyses of multiple existing (and new) pre-processing and unsupervised domain adaptation methods. We conduct a series of comprehensive experiments and derive three main observations. (1) Pre-processing and unsupervised domain adaptation methods can enhance the portability of deep learning side-channel attacks over more challenging targets. (2) The effectiveness of each method, however, varies depending on the target and probe locations in use. In other words, observations of a method on easy targets do not necessarily generalize to challenging targets. (3) None of the methods can constantly outperform others. Moreover, we highlight two types of pitfalls that could lead to over-optimistic attack results in cross-device evaluations. We also contribute a large-scale public dataset (with 3 million EM traces from 9 probe locations over multiple targets) for benchmarking and reproducibility of side-channel attacks tackling domain shifts over EM traces.