TY - JOUR
T1 - NASCTY
T2 - Neuroevolution to Attack Side-Channel Leakages Yielding Convolutional Neural Networks
AU - Schijlen, Fiske
AU - Wu, Lichao
AU - Mariot, Luca
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - genetic algorithm (GA)
KW - neural architecture search (NAS)
KW - neural network (NN)
KW - side-channel analysis (SCA)
UR - http://www.scopus.com/inward/record.url?scp=85164201422&partnerID=8YFLogxK
U2 - 10.3390/math11122616
DO - 10.3390/math11122616
M3 - Article
AN - SCOPUS:85164201422
SN - 2227-7390
VL - 11
JO - Mathematics
JF - Mathematics
IS - 12
M1 - 2616
ER -