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Title:
Conditional Variational AutoEncoder based on Stochastic Attack
Authors: Gabriel Zaid, Lilian Bossuet, Mathieu Carbone, Amaury Habrard, 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.
ePrint: https://eprint.iacr.org/2022/232
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