@article{Cao_Zhang_Lu_Gu_2021, title={Cross-Device Profiled Side-Channel Attack with Unsupervised Domain Adaptation}, volume={2021}, url={https://tches.iacr.org/index.php/TCHES/article/view/9059}, DOI={10.46586/tches.v2021.i4.27-56}, abstractNote={<p>Deep learning (DL)-based techniques have recently proven to be very successful when applied to profiled side-channel attacks (SCA). In a real-world profiled SCA scenario, attackers gain knowledge about the target device by getting access to a similar device prior to the attack. However, most state-of-the-art literature performs only proof-of-concept attacks, where the traces intended for profiling and attacking are acquired consecutively on the same fully-controlled device. This paper reminds that even a small discrepancy between the profiling and attack traces (regarded as domain discrepancy) can cause a successful single-device attack to completely fail. To address the issue of domain discrepancy, we propose a Cross-Device Profiled Attack (CDPA), which introduces an additional fine-tuning phase after establishing a pretrained model. The fine-tuning phase is designed to adjust the pre-trained network, such that it can learn a hidden representation that is not only discriminative but also domain-invariant. In order to obtain domain-invariance, we adopt a maximum mean discrepancy (MMD) loss as a constraint term of the classic cross-entropy loss function. We show that the MMD loss can be easily calculated and embedded in a standard convolutional neural network. We evaluate our strategy on both publicly available datasets and multiple devices (eight Atmel XMEGA 8-bit microcontrollers and three SAKURA-G evaluation boards). The results demonstrate that CDPA can improve the performance of the classic DL-based SCA by orders of magnitude, which significantly eliminates the impact of domain discrepancy caused by different devices.</p>}, number={4}, journal={IACR Transactions on Cryptographic Hardware and Embedded Systems}, author={Cao, Pei and Zhang, Chi and Lu, Xiangjun and Gu, Dawu}, year={2021}, month={Aug.}, pages={27–56} }