Paper 2023/1108
It's a Kind of Magic: A Novel Conditional GAN Framework for Efficient Profiling Side-channel Analysis
Abstract
Profiling side-channel analysis is an essential technique to assess the security of protected cryptographic implementations by subjecting them to the worst-case security analysis. This approach assumes the presence of a highly capable adversary with knowledge of countermeasures and randomness employed by the target device. However, black-box profiling attacks are commonly employed when aiming to emulate real-world scenarios. These attacks leverage deep learning as a prominent alternative since deep neural networks can automatically select points of interest, eliminating the need for secret mask knowledge. Nevertheless, black-box profiling attacks often result in non-worst-case security evaluations, leading to suboptimal profiling models. In this study, we propose modifying the conventional black-box threat model by incorporating a new assumption: the adversary possesses a similar implementation that can be used as a white-box reference design. We create an adversarial dataset by extracting features or points of interest from this reference design. These features are then utilized for training a novel conditional generative adversarial network (CGAN) framework, enabling a generative model to extract features from high-order leakages in protected implementation without any assumptions about the masking scheme or secret masks. Our framework empowers attackers to perform efficient black-box profiling attack that achieves (and even surpasses) the performance of the worst-case security assessments.
Metadata
- Available format(s)
- Category
- Attacks and cryptanalysis
- Publication info
- Preprint.
- Keywords
- Side-Channel AnalysisDeep LearningGenerative AI
- Contact author(s)
-
s karayalcin @ liacs leidenuniv nl
m krcek @ tudelft nl
l wu-4 @ tudelft nl
stjepan picek @ ru nl
guilhermeperin7 @ gmail com - History
- 2023-07-17: approved
- 2023-07-16: received
- See all versions
- Short URL
- https://ia.cr/2023/1108
- License
-
CC0
BibTeX
@misc{cryptoeprint:2023/1108, author = {Sengim Karayalcin and Marina Krcek and Lichao Wu and Stjepan Picek and Guilherme Perin}, title = {It's a Kind of Magic: A Novel Conditional GAN Framework for Efficient Profiling Side-channel Analysis}, howpublished = {Cryptology ePrint Archive, Paper 2023/1108}, year = {2023}, note = {\url{https://eprint.iacr.org/2023/1108}}, url = {https://eprint.iacr.org/2023/1108} }