InfoNEAT: Information Theory-based NeuroEvolution of Augmenting Topologies for Side-channel Analysis

TL;DR

This paper introduces InfoNEAT, an automated neural architecture search method guided by information theory, significantly improving side-channel attack efficiency and robustness over traditional neural network approaches.

Contribution

InfoNEAT presents a novel neural structure search method using information-theoretic metrics, optimizing neural network configuration and training stopping criteria for side-channel analysis.

Findings

InfoNEAT is up to 6 times faster in training.

It reduces the number of attack traces needed by up to 1000 times.

Models are robust against noise and desynchronization.

Abstract

Profiled side-channel analysis (SCA) leverages leakage from cryptographic implementations to extract the secret key. When combined with advanced methods in neural networks (NNs), profiled SCA can successfully attack even those crypto-cores assumed to be protected against SCA. Despite the rise in the number of studies devoted to NN-based SCA, a range of questions has remained unanswered, namely: how to choose an NN with an adequate configuration, how to tune the NN's hyperparameters, when to stop the training, etc. Our proposed approach, ``InfoNEAT,'' tackles these issues in a natural way. InfoNEAT relies on the concept of neural structure search, enhanced by information-theoretic metrics to guide the evolution, halt it with novel stopping criteria, and improve time-complexity and memory footprint. The performance of InfoNEAT is evaluated by applying it to publicly available datasets composed of real side-channel measurements. In addition to the considerable advantages regarding the automated configuration of NNs, InfoNEAT demonstrates significant improvements over other approaches for effective key recovery in terms of the number of epochs (e.g.,x6 faster) and the number of attack traces compared to both MLPs and CNNs (e.g., up to 1000s fewer traces to break a device) as well as a reduction in the number of trainable parameters compared to MLPs (e.g., by the factor of up to 32). Furthermore, through experiments, it is demonstrated that InfoNEAT's models are robust against noise and desynchronization in traces.