International Association
for Cryptologic Research

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 and powerful models a notoriously hard problem. This can be problematic during an evaluation process where a security bound is required. In this work, we propose the first solution that bridges DL and SCA in order to get this security bound. Based on theoretical results, we develop the first Machine Learning generative model, called Conditional 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 F2n . We validate our theoretical proposition through simulations and public datasets on a wide range of use cases, including multi-task learning, curse of dimensionality and masking scheme.

This paper presents the results of several successful profiled side-channel attacks against a secure implementation of the RSA algorithm. The implementation was running on a ARM Core SC 100 completed with a certified EAL4+ arithmetic co-processor. The analyses have been conducted by three experts’ teams, each working on a specific attack path and exploiting information extracted either from the electromagnetic emanation or from the power consumption. A particular attention is paid to the description of all the steps that are usually followed during a security evaluation by a laboratory, including the acquisitions and the observations preprocessing which are practical issues usually put aside in the literature. Remarkably, the profiling portability issue is also taken into account and different device samples are involved for the profiling and testing phases. Among other aspects, this paper shows the high potential of deep learning attacks against secure implementations of RSA and raises the need for dedicated countermeasures.