The absence of an algorithm that effectively monitors the deep learning models used in side-channel attacks increases the difficulty of a security evaluation. If an attack is unsuccessful, that could be due to multiple reasons. It can be that we are indeed dealing with a resistant implementation, but it is possible that the deep learning model used is faulty. In this contribution, we formalize two conditions,persistenceandpatience, for a deep learning model to be optimal and we propose an early stopping algorithm that reliably recognizes the model's optimal state during training. The novelty of our solution is in an efficient implementation of guessing entropy estimation as a success metric used to measure the strength of a side-channel adversary. As a result, the model which uses our strategy for learning converges with fewer traces than other known methods.

This paper introduces a deep learning modular network for side-channel analysis. Our approach features a deep learning architecture with the capability to exchange parts (modules) with other neural networks. We aim to introduce reusable trained modules into side-channel analysis instead of building architectures from scratch for each evaluation, reducing the body of work. Our experiments demonstrate that our architecture feasibly assesses a side-channel evaluation, suggesting that learning transferability is possible using the architecture we propose in this paper.

In the field of side-channel analysis, profiled attacks are one of the most powerful types of attacks. Nevertheless, major issues with profiled attacks are in sensitivity to noise and the high-dimensional nature of the signals used for training, generating a less efficient classifier to conduct the attack phase. Consequently, evaluating the security of cryptographic implementation in hardware devices like IoT becomes more complex as side-channel analysis evaluation easily falls into false-positive results. This paper assesses the efficacy of applying a feature reduction process to deal with high-dimensional signals. We propose a practical procedure to conduct feature reduction using autoencoders for profiled side-channel leakage evaluations. Two autoencoder architectures are compared while performing feature reduction showing that our proposed architecture keeps most of the relevant information. Our proposal is tested on the ASCAD random key database with a high desynchronization value and produced results that outperform other state-of-the-art techniques. The guessing entropy value converges to 1 after around 500 leakage traces.

Performing a comprehensive side-channel analysis evaluation of small embedded devices is a process known for its variability and complexity. In real-world experimental setups, the results are largely influenced by a huge amount of parameters, some of which are not easily adjusted without trial and error and are heavily relying on the experience of professional security analysts. In this article, we advocate the usage of an existing statistical methodology called Six Sigma (6 ) for side-channel analysis optimization. This well-known methodology is commonly used in other industrial fields, such as production and quality engineering, to reduce the variability of industrial processes. We propose a customized Six Sigma methodology, which allows even a less-experienced security analysis to select optimal values for the different variables that are critical for the side-channel analysis procedure. Moreover, we show how our methodology helps in improving different phases in the side-channel analysis process.