NASCTY

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

Schijlen, Fiske (Student TU Delft)
Wu, Lichao (Student TU Delft)
Mariot, L. (TU Delft Cyber Security; University of Twente)

2023

Side-channel analysis (SCA) is a class of attacks on the physical implementation of a cipher, which enables the extraction of confidential key information by exploiting unintended leaks generated by a device. In recent years, researchers have observed that neural networks (NNs) can be utilized to perform highly effective SCA profiling, even against countermeasure-hardened targets. This study investigates a new approach to designing NNs for SCA, called neuroevolution to attack side-channel traces yielding convolutional neural networks (NASCTY-CNNs). This method is based on a genetic algorithm (GA) that evolves the architectural hyperparameters to automatically create CNNs for side-channel analysis. The findings of this research demonstrate that we can achieve performance results comparable to state-of-the-art methods when dealing with desynchronized leakages protected by masking techniques. This indicates that employing similar neuroevolutionary techniques could serve as a promising avenue for further exploration. Moreover, the similarities observed among the constructed neural networks shed light on how NASCTY effectively constructs architectures and addresses the implemented countermeasures.

genetic algorithm (GA)
neural architecture search (NAS)
neural network (NN)
side-channel analysis (SCA)

http://resolver.tudelft.nl/uuid:5fe00d29-0427-454d-8ac0-1fc22ecf8b73

https://doi.org/10.3390/math11122616

2227-7390

Mathematics, 11 (12)

Institutional Repository

journal article

© 2023 Fiske Schijlen, Lichao Wu, L. Mariot