Online performance evaluation of deep learning networks for profiled side-channel analysis

DOI10.1007/978-3-030-68773-1_10OpenAlexW3003493139MaRDI QIDQ2106696

Damien Robissout, Gabriel Zaid, Lilian Bossuet, Brice Colombier, Amaury Habrard

Publication date: 16 December 2022

Full work available at URL: https://doi.org/10.1007/978-3-030-68773-1_10

zbMATH Keywords

metricsdeep learningoverfittingside-channel attacksunderfitting

Mathematics Subject Classification ID

Cryptography (94A60) Artificial intelligence (68Txx)

Related Items (2)

Learning when to stop: a mutual information approach to prevent overfitting in profiled side-channel analysis ⋮ Focus is key to success: a focal loss function for deep learning-based side-channel analysis

Cites Work

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• Breaking cryptographic implementations using deep learning techniques
• A Unified Framework for the Analysis of Side-Channel Key Recovery Attacks
• Convolutional Neural Networks with Data Augmentation Against Jitter-Based Countermeasures

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