Abstract
Independent Component Analysis (ICA) is a powerful technique for blind source separation. It has been successfully applied to signal processing problems, such as feature extraction and noise reduction, in many different areas including medical signal processing and telecommunication. In this work, we propose a framework to apply ICA to denoise side-channel measurements and hence to reduce the complexity of key recovery attacks. Based on several case studies, we afterwards demonstrate the overwhelming advantages of ICA with respect to the commonly used preprocessing techniques such as the singular spectrum analysis. Mainly, we target a software masked implementation of an AES and a hardware unprotected one. Our results show a significant Signal-to-Noise Ratio (SNR) gain which translates into a gain in the number of traces needed for a successful side-channel attack. This states the ICA as an important new tool for the security assessment of cryptographic implementations.
H. Maghrebi and E. Prouff—This work has been done when the authors was working at Safran Identity and Security.
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Notes
- 1.
Note that we used the notation \(\mathbf {s}_1^{(m)}\) to alert on the fact that the signal \(s_1\) corresponds to the plaintext m.
- 2.
Another option could consist in only using a few number of measurements (e.g. 100) for each value m in order to speed up the execution of our algorithm.
- 3.
This threshold is defined for one m value (e.g. \(m=0\)) and then applied for the other ones. We stress the fact that other approaches could be applied to distinguish the genuine signal from the noise. For instance, one can (1) compute the correlation between the noisy signal and the obtained source signals or (2) apply a dimensionality reduction algorithm (e.g. PCA or LDA).
- 4.
A LeCroy WavePro 725Zi oscilloscope with maximum 40 GS/s sampling rate and an active differential probe Lecroy ZD1500 have been used to measure the voltage drop over a \(1\varOmega \) resistor in the VDD path.
- 5.
It merely consists in replacing the fifth step in Algorithm 1 by an averaging of the traces in \(\mathbf {X}^{(m)}\).
- 6.
We recall that other filtering techniques exist, e.g. the wavelet [18], but are not considered in our work since are heuristic methods.
- 7.
We stress the fact that same results were obtained when targeting the other SBoxes and are not shown here for lack of room.
- 8.
Particular attention has been paid on the implementation to ensure that no first-order leakage occurred.
- 9.
On other protected implementations, we observed that the gain with ICA techniques is more important. However, we cannot communicate information related to these implementations and the tested chips since these are confidential IPs.
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A Example of Trace Denoising Based on the FastICA Method
A Example of Trace Denoising Based on the FastICA Method
For illustration, an exemplary power trace and the resulting filtered trace after applying ICA are shown in Fig. 4.
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Maghrebi, H., Prouff, E. (2018). On the Use of Independent Component Analysis to Denoise Side-Channel Measurements. In: Fan, J., Gierlichs, B. (eds) Constructive Side-Channel Analysis and Secure Design. COSADE 2018. Lecture Notes in Computer Science(), vol 10815. Springer, Cham. https://doi.org/10.1007/978-3-319-89641-0_4
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