Subsampling and knowledge distillation on adversarial examples: new techniques for deep learning based side channel evaluations

DOI10.1007/978-3-030-81652-0_22zbMath1493.94033OpenAlexW3184130264MaRDI QIDQ832386

Werner Schindler, Aron Gohr, Sven Jacob

Publication date: 25 March 2022

Full work available at URL: https://doi.org/10.1007/978-3-030-81652-0_22

zbMATH Keywords

machine learningSAT solverdeep learningpower analysis

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Cryptography (94A60)

Related Items (1)

TransNet: shift invariant transformer network for side channel analysis

Uses Software

• Scikit
• CryptoMiniSat
• Keras
• Adam
• GitHub
• deep_speck
• PRMLT

Cites Work

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• Deep neural network attribution methods for leakage analysis and symmetric key recovery
• Improving attacks on round-reduced Speck32/64 using deep learning
• Convolutional Neural Networks with Data Augmentation Against Jitter-Based Countermeasures
• On the performance of convolutional neural networks for side-channel analysis

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