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

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 pe...

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Bibliographic Details
Published in:Mathematics (Basel) 2023-06, Vol.11 (12), p.2616
Main Authors: Schijlen, Fiske, Wu, Lichao, Mariot, Luca
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Artificial neural networks
Automation
Candidates
Cloning
Datasets
Encryption
genetic algorithm (GA)
Genetic algorithms
neural architecture search (NAS)
neural network (NN)
Neural networks
Optimization
Probability
side-channel analysis (SCA)
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11122616