A Second Look at the Portability of Deep Learning Side-Channel Attacks over EM Traces
Mabon Ninan, Evan Nimmo, Shane Reilly, Channing Smith, Wenhai Sun, Boyang Wang, John M. Emmert · 2024
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).