Cryptology ePrint Archive: Report 2022/232

Conditional Variational AutoEncoder based on Stochastic Attack

Gabriel Zaid and Lilian Bossuet and Mathieu Carbone and Amaury Habrard and Alexandre Venelli

Abstract: Over the recent years, the cryptanalysis community leveraged the potential of research on Deep Learning to enhance attacks. In particular, several studies have recently highlighted the benefits of Deep Learning based Side-Channel Attacks (DLSCA) to target real-world cryptographic implementations. While this new research area on applied cryptography provides impressive result to recover a secret key even when countermeasures are implemented (e.g. desynchronization, masking schemes), the lack of theoretical results make the construction of appropriate models a notoriously hard problem. In this work, we propose the first solution that bridges DL and SCA. Based on theoretical results, we develop the first generative model, called Conditionnal Variational AutoEncoder based on Stochastic Attacks (cVAE-SA), designed from the well-known Stochastic Attacks, that have been introduced by Schindler et al. in $2005$. This model reduces the black-box property of DL and eases the architecture design for every real-world crypto-system as we define theoretical complexity bounds which only depend on the dimension of the (reduced) trace and the targeting variable over $\mathbb{F}_{2}^{n}$. We validate our theoretical proposition through simulations and public datasets on wide-range of use-cases, including multi-task learning, curse of dimensionality and masking scheme.

Category / Keywords: secret-key cryptography / Side-Channel Attacks and Deep Learning and Discriminative Models and Generative Models and Stochastic Attacks and Variational AutoEncoder

Date: received 23 Feb 2022, last revised 23 Feb 2022

Contact author: gabriel zaid at thalesgroup com

Available format(s): PDF | BibTeX Citation

Version: 20220225:080735 (All versions of this report)

Short URL: ia.cr/2022/232


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