| Autor: |
Pei Cao, Chi Zhang, Xiangjun Lu, Dawu Gu |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Transactions on Cryptographic Hardware and Embedded Systems, Vol 2021, Iss 4 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2569-2925 |
| DOI: |
10.46586/tches.v2021.i4.27-56 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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