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Journal of Cryptographic Engineering

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30-11-2019 | Regular Paper

Deep learning for side-channel analysis and introduction to ASCAD database

Authors: Ryad Benadjila, Emmanuel Prouff, Rémi Strullu, Eleonora Cagli, Cécile Dumas

Published in: Journal of Cryptographic Engineering | Issue 2/2020

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Abstract

Recent works have demonstrated that deep learning algorithms were efficient to conduct security evaluations of embedded systems and had many advantages compared to the other methods. Unfortunately, their hyper-parametrization has often been kept secret by the authors who only discussed on the main design principles and on the attack efficiencies in some specific contexts. This is clearly an important limitation of previous works since (1) the latter parametrization is known to be a challenging question in machine learning and (2) it does not allow for the reproducibility of the presented results and (3) it does not allow to draw general conclusions. This paper aims to address these limitations in several ways. First, completing recent works, we propose a study of deep learning algorithms when applied in the context of side-channel analysis and we discuss the links with the classical template attacks. Secondly, for the first time, we address the question of the choice of the hyper-parameters for the class convolutional neural networks. Several benchmarks and rationales are given in the context of the analysis of a challenging masked implementation of the AES algorithm. Interestingly, our work shows that the approach followed to design the algorithm VGG-16 used for image recognition seems also to be sound when it comes to fix an architecture for side-channel analysis. To enable perfect reproducibility of our tests, this work also introduces an open platform including all the sources of the target implementation together with the campaign of electromagnetic measurements exploited in our benchmarks. This open database, named ASCAD, is the first one in its category and it has been specified to serve as a common basis for further works on this subject.

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Some libraries (such as hyperopt or hyperas, [8]) could have been tested to automatize the search of accurate hyper-parameters in pre-defined sets. However, since they often perform a random search of the best parameters ([7]), they do not allow studying the impact of each hyper-parameter independently of the others on the side-channel attack success rate. Moreover, they have been defined to maximize classical machine learning evaluation metrics and not SCA ranking functions which require a batch of test traces.

We have validated that the code and the full project can be easily tested with the Chipwhisperer platform developed by C. O’ Flynn [52].

In Templates Attacks the profiling set and the attack set are assumed to be different, namely the traces \(\varvec{\ell }_{i}\) involved in (2) have not been used for the profiling.

The name generative is due to the fact that it is possible to generate synthetic traces by sampling from such probability distributions.

When no ambiguity is present we will call simply hyper-parameters the architecture ones.

We insist here on the fact that the model is trained from scratch at each iteration of the loop over t.

and also different values of \(k^{\star }\) if this is relevant for the attacked algorithm.

Another metric, the prediction error (PE), is sometimes used in combination with the accuracy: it is defined as the expected error of the model over the training sets; \(\mathsf {PE}_{N_{\text {train}}}(\hat{\mathbf {\text {g}}}_{}) = 1 - \mathsf {ACC}_{N_{\text {train}}}(\hat{\mathbf {\text {g}}}_{})\).

The SNR is sometimes named F-Test to refer to its original introduction by Fischer [18]. For a noisy observation \(L_{t}\) at time sample t of an event \(Z\), it is defined as \(\mathsf {Var}[\mathsf {E}_{}[L_{t}\mid Z ] ]/\mathsf {E}_{}[\mathsf {Var}[L_{t}\mid Z ] ]\).

Another possibility would have been to target \(\text {state0}[3] = \text {sbox}(p[3]\oplus k[3])\oplus r[3]\) which is manipulated at the end of Step 8] in Algorithm 1.

Note that some peaks appearing in Fig. 1b have not been selected.

They are called Fully-Connected because each i-th input coordinate is connected to each j-th output via the \(\mathbf{A}[i,j]\) weight. FC layers can be seen as a special case of the linear layers where not all the connections are necessarily present. The absence of some (i, j) connections can be formalized as a constraint for the matrix \(\mathbf{A}\) consisting in forcing to 0 its (i, j)-th coordinates.

Amount of units by which a filter shifts across the trace.

patches in the machine learning language.

Ambiguity: Neural networks with many layers are sometimes called Deep Neural Networks, where the depth corresponds to the number of layers.

To prevent underflow, the log-softmax is usually preferred if several classification outputs must be combined.

where each layer of the same type appearing in the composition is not to be intended as exactly the same function (e.g. with same input/output dimensions), but as a function of the same form.

Straightforwardly customized to apply on 1-dimensional inputs of 700 units and outputs of 256 units.

Leading to 10 training sets of size 45,000 and 10 test sets of size 5000 to perform the 10-fold cross-validation.

Having 50 epochs and a batch size equal to 50 is also a good trade-off, but between two options that seem equivalent, we chose to prefer the solution with the highest number of epochs.

For the sake of completeness, we have also tested the SCANet model introduced in [54]. This did not yield to good performances on our dataset: we have obtained a mean rank of approximatively 128 for each of our desynchronizations 0, 50 and 100.

Additionally, beware that in this paper training and testing are used in the context of cross-validation and are subsets of the profiling dataset \(\mathcal {D}_{\text {profiling}}\).

We recommend to perform the cross-validation only with the profiling set.

Stride pooling consists in taking the first value on each input window defined by the stride.

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Title: Deep learning for side-channel analysis and introduction to ASCAD database
Authors: Ryad Benadjila
Emmanuel Prouff
Rémi Strullu
Eleonora Cagli
Cécile Dumas
Publication date: 30-11-2019
Publisher: Springer Berlin Heidelberg
Published in: Journal of Cryptographic Engineering / Issue 2/2020
Print ISSN: 2190-8508
Electronic ISSN: 2190-8516
DOI: https://doi.org/10.1007/s13389-019-00220-8

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