Paper 2021/1107
Multi-Leak Deep-Learning Side-Channel Analysis
Fanliang Hu and Huanyu Wang and Junnian Wang
Abstract
Deep Learning Side-Channel Attacks (DLSCAs) have become a realistic threat to implementations of cryptographic algorithms, such as Advanced Encryption Standard (AES). By utilizing deep-learning models to analyze side-channel measurements, the attacker is able to derive the secret key of the cryptographic alrgorithm. However, when traces have multiple leakage intervals for a specific attack point, the majority of existing works train neural networks on these traces directly, without a preprocess step for each leakage interval. This degenerates the quality of profiling traces due to noise and non-primary components. In this paper, we first divide the multi-leaky traces into leakage intervals and train models on different intervals separately. Afterwards, we concatenate these neural networks to build the final network, which is called multi-input model. We test the proposed multi-input model on traces captured from STM32F3 microcontroller implementations of AES-128 and show a 2-fold improvement over the previous single-input attacks.
Metadata
- Available format(s)
- Publication info
- Preprint. MINOR revision.
- Keywords
- Side-channel attacksMultiple leakageMulti-input modelAESDeep learning
- Contact author(s)
- fanliang @ mail hnust edu cn
- History
- 2022-03-06: revised
- 2021-08-31: received
- See all versions
- Short URL
- https://ia.cr/2021/1107
- License
-
CC BY