A deeper look at machine learning-based cryptanalysis

DOI10.1007/978-3-030-77870-5_28OpenAlexW3159555774MaRDI QIDQ2056717

Quan Quan Tan, David Gérault, Thomas Peyrin, Adrien Benamira

Publication date: 8 December 2021

Full work available at URL: https://doi.org/10.1007/978-3-030-77870-5_28

zbMATH Keywords

SPECKinterpretabilitymachine learningdifferential cryptanalysisdeep neural networks

Mathematics Subject Classification ID

Cryptography (94A60) Data encryption (aspects in computer science) (68P25) Artificial intelligence (68Txx)

Related Items (4)

NNBits: bit profiling with a deep learning ensemble based distinguisher ⋮ Efficient detection of high probability statistical properties of cryptosystems via surrogate differentiation ⋮ Enhancing differential-neural cryptanalysis ⋮ Improved differential-linear attack with application to round-reduced Speck32/64

Cites Work

• Greedy function approximation: A gradient boosting machine.
• Polynomial calculation of the Shapley value based on sampling
• Breaking cryptographic implementations using deep learning techniques
• Improving attacks on round-reduced Speck32/64 using deep learning
• Improved Differential Cryptanalysis of Round-Reduced Speck
• Automatic Differential Analysis of ARX Block Ciphers with Application to SPECK and LEA
• Differential Cryptanalysis of Round-Reduced Simon and Speck
• Differential Analysis of Block Ciphers SIMON and SPECK
• The Simon and Speck Block Ciphers on AVR 8-Bit Microcontrollers
• MILP-Based Automatic Search Algorithms for Differential and Linear Trails for Speck
• Differential and Linear Cryptanalysis Using Mixed-Integer Linear Programming
• Random forests
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