Paper 2020/867
Enhancing the Performance of Practical Profiling Side-Channel Attacks Using Conditional Generative Adversarial Networks
Ping Wang, Ping Chen, Zhimin Luo, Gaofeng Dong, Mengce Zheng, Nenghai Yu, and Honggang Hu
Abstract
Recently, many profiling side-channel attacks based on Machine Learning and Deep Learning have been proposed. Most of them focus on reducing the number of traces required for successful attacks by optimizing the modeling algorithms. In previous work, relatively sufficient traces need to be used for training a model. However, in the practical profiling phase, it is difficult or impossible to collect sufficient traces due to the constraint of various resources. In this case, the performance of profiling attacks is inefficient even if proper modeling algorithms are used. In this paper, the main problem we consider is how to conduct more efficient profiling attacks when sufficient profiling traces cannot be obtained. To deal with this problem, we first introduce the Conditional Generative Adversarial Network (CGAN) in the context of side-channel attacks. We show that CGAN can generate new traces to enlarge the size of the profiling set, which improves the performance of profiling attacks. For both unprotected and protected cryptographic algorithms, we find that CGAN can effectively learn the leakage of traces collected in their implementations. We also apply it to different modeling algorithms. In our experiments, the model constructed with the augmented profiling set can reduce the required attack traces by more than half, which means the generated traces can provide useful information as the real traces.
Metadata
- Available format(s)
-
PDF
- Category
- Implementation
- Publication info
- Preprint. MINOR revision.
- Keywords
- Profiling side-channel attacksCGANGenerated tracesLeakage learningInsufficient profiling set
- Contact author(s)
- mczheng @ mail ustc edu cn
- History
- 2021-01-18: revised
- 2020-07-12: received
- See all versions
- Short URL
- https://ia.cr/2020/867
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2020/867,
author = {Ping Wang and Ping Chen and Zhimin Luo and Gaofeng Dong and Mengce Zheng and Nenghai Yu and Honggang Hu},
title = {Enhancing the Performance of Practical Profiling Side-Channel Attacks Using Conditional Generative Adversarial Networks},
howpublished = {Cryptology {ePrint} Archive, Paper 2020/867},
year = {2020},
url = {https://eprint.iacr.org/2020/867}
}