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

Fiske Schijlen, Lichao Wu and Luca Mariot ()
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Fiske Schijlen: Cybersecurity Research Group, Delft University of Technology, Mekelweg 5, 2628 CD Delft, The Netherlands
Lichao Wu: Cybersecurity Research Group, Delft University of Technology, Mekelweg 5, 2628 CD Delft, The Netherlands
Luca Mariot: Semantics, Cybersecurity and Services Group, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands

Mathematics, 2023, vol. 11, issue 12, 1-20

Abstract: 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.

Keywords: side-channel analysis (SCA); genetic algorithm (GA); neural network (NN); neural architecture search (NAS) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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