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Kilroy Was Here: The First Step Towards Explainability of Neural Networks in Profiled Side-Channel Analysis

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Constructive Side-Channel Analysis and Secure Design (COSADE 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12244))

Abstract

While several works have explored the application of deep learning for efficient profiled side-channel analysis, explainability, or, in other words, what neural networks learn remains a rather untouched topic. As a first step, this paper explores the Singular Vector Canonical Correlation Analysis (SVCCA) tool to interpret what neural networks learn while training on different side-channel datasets, by concentrating on deep layers of the network. Information from SVCCA can help, to an extent, with several practical problems in a profiled side-channel analysis like portability issue and criteria to choose a number of layers/neurons to fight portability, provide insight on the correct size of training dataset and detect deceptive conditions like over-specialization of networks.

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Acknowledgment

The authors acknowledge the support from the ’National Integrated Centre of Evaluation’ (NICE); a facility of Cyber Security Agency, Singapore (CSA).

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Authors and Affiliations

  1. Delft University of Technology, Delft, The Netherlands

    Daan van der Valk & Stjepan Picek

  2. Physical Analysis and Cryptographic Engineering, Temasek Laboratories at Nanyang Technological University, Singapore, Singapore

    Shivam Bhasin

Authors
  1. Daan van der Valk
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  2. Stjepan Picek
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  3. Shivam Bhasin
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Corresponding author

Correspondence to Stjepan Picek .

Editor information

Editors and Affiliations

  1. RD, Security Pattern SRL, Brescia, Brescia, Italy

    Guido Marco Bertoni

  2. AlaRI, Università della Svizzera italiana, Lugano, Switzerland

    Francesco Regazzoni

A Additional Figures

A Additional Figures

In this section, we depict the results for hidden layers we omitted from the paper’s main body due to their similar behavior as those already presented. In Fig. 9, we depict the correlation results for the second hidden layer of an MLP in the intermediate value leakage model. Similarly, Fig. 10 shows results for the MLP2 architecture, the Hamming weight leakage model for hidden layers 2 and 3.

In Fig. 12, we show the results for the label-based inspection approach for the MLP2 architecture. We consider a scenario where both device and keys differ for the intermediate value leakage model for hidden layers 2 and 3.

Fig. 11.
figure 11

Correlation results for MLP2 in the intermediate value leakage model for hidden layers 2 and 3.

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Fig. 12.
figure 12

Label-based inspection for MLP2 when both devices and keys differ, intermediate value leakage model for hidden layers 2 and 3.

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Figure 13 gives correlation results for CNN for the first fully-connected layers for both the Hamming weight and intermediate value leakage models.

Fig. 13.
figure 13

Correlation results for CNN for the first fully-connected layer.

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van der Valk, D., Picek, S., Bhasin, S. (2021). Kilroy Was Here: The First Step Towards Explainability of Neural Networks in Profiled Side-Channel Analysis. In: Bertoni, G.M., Regazzoni, F. (eds) Constructive Side-Channel Analysis and Secure Design. COSADE 2020. Lecture Notes in Computer Science(), vol 12244. Springer, Cham. https://doi.org/10.1007/978-3-030-68773-1_9

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  • DOI: https://doi.org/10.1007/978-3-030-68773-1_9

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  • Online ISBN: 978-3-030-68773-1

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