NASCTY: Neuroevolution to Attack Side-channel Leakages Yielding Convolutional Neural Networks

TL;DR

This paper introduces NASCTY, a neuroevolution-based method that automatically designs CNN architectures for side-channel analysis, effectively attacking protected devices and revealing insights into neural network design for security.

Contribution

The paper presents NASCTY, a novel genetic algorithm approach for automatically evolving CNN architectures tailored for side-channel attack scenarios.

Findings

Achieves near state-of-the-art performance on desynchronized, masked leakages.

Demonstrates the effectiveness of neuroevolution in designing attack architectures.

Provides insights into neural network features that counteract countermeasures.

Abstract

Side-channel analysis (SCA) can obtain information related to the secret key by exploiting leakages produced by the device. Researchers recently found that neural networks (NNs) can execute a powerful profiling SCA, even on targets protected with countermeasures. This paper explores the effectiveness of Neuroevolution to Attack Side-channel Traces Yielding Convolutional Neural Networks (NASCTY-CNNs), a novel genetic algorithm approach that applies genetic operators on architectures' hyperparameters to produce CNNs for side-channel analysis automatically. The results indicate that we can achieve performance close to state-of-the-art approaches on desynchronized leakages with mask protection, demonstrating that similar neuroevolution methods provide a solid venue for further research. Finally, the commonalities among the constructed NNs provide information on how NASCTY builds effective architectures and deals with the applied countermeasures.