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

Autor:	Schijlen, Fiske, Wu, Lichao, Mariot, Luca
Rok vydání:	2023
Předmět:	Computer Science - Neural and Evolutionary Computing Computer Science - Cryptography and Security
Druh dokumentu:	Working Paper
Popis:	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. Comment: 19 pages, 6 figures, 4 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2301.10802 Zobrazit plný text záznamu View this record from Arxiv