Abstract
Side-Channel Analysis (SCA) plays a crucial role in hardware security evaluation. However, side-channel acquisitions (a.k.a. traces) usually contain noises that often impose negative effects on key-recovery efficiency. In this paper, we propose convolutional denoising autoencoder (CDAE) for noise reduction in SCA. CDAE is composed of multiple layers of convolution operators, learning an end-to-end mapping from noisy traces to clean traces by minimizing the \(\ell _2\) loss of noisy-clean trace pairs. The convolutional layers capture the abstraction of the traces while eliminating noises. We argue that CDAE is very suitable for profiled SCA especially when the attacker has a large amount of traces in the offline profiling phase. Once the network training is done, our denoising network can be applied to individual new noisy traces for the attacker to launch online attacks. To validate the effectiveness of our method, we train CDAE to denoise traces and then perform Template Attacks (TA) in three high noise jamming scenarios, including unprotected (GPU and FPGA based) and protected (MCU based) AES implementations. Our method can significantly outperform the state-of-the-art Singular Spectrum Analysis (SSA) denoising method on both information theoretic metrics and security metrics. Results show that CDAE achieves at least \(\sim 4\times \) Signal-to-Noise Ratio (SNR) gain, thus TA with denoising preprocessing requires at most 50% of the traces in the attack phase.
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Notes
- 1.
Totally 256 classes for AES implementation in this paper because there are 256 elements in Galois field \(GF(2^8)\).
- 2.
We stress that the information gain remains consistency in \(\mathcal {F}(\mathcal {X}_{\text {profiling}})\) and \(\mathcal {F}(\mathcal {X}_{\text {attack}})\) since early-stopping is used to prevent over-fitting.
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Acknowledgment
This work was supported in part by the National Natural Science Foundation of China (No. 61632020) and Beijing Natural Science Foundation (No. 4192067).
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Appendix A. Kernel Density Estimation of Univariate Distribution at PoI
Appendix A. Kernel Density Estimation of Univariate Distribution at PoI
Power distribution of sensitive variable v at PoI 276 (256 classes). (Color figure online)
Power distributions of sensitive variable v at PoI 330 (256 classes). (Color figure online)
Power distributions of masked Sbox output v at PoI 517 and mask m at PoI 156. (Color figure online)
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Yang, G., Li, H., Ming, J., Zhou, Y. (2020). CDAE: Towards Empowering Denoising in Side-Channel Analysis. In: Zhou, J., Luo, X., Shen, Q., Xu, Z. (eds) Information and Communications Security. ICICS 2019. Lecture Notes in Computer Science(), vol 11999. Springer, Cham. https://doi.org/10.1007/978-3-030-41579-2_16
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