Differential-ML distinguisher: machine learning based generic extension for differential cryptanalysis

DOI10.1007/978-3-030-88238-9_10zbMath1491.94073OpenAlexW3208405702MaRDI QIDQ2146092

Manoj Kumar, Tarun Yadav

Publication date: 15 June 2022

Full work available at URL: https://doi.org/10.1007/978-3-030-88238-9_10

zbMATH Keywords

block ciphermachine learningdifferential cryptanalysis

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Cryptography (94A60) Data encryption (aspects in computer science) (68P25)

Related Items (3)

MILP based differential attack on round reduced WARP ⋮ NNBits: bit profiling with a deep learning ensemble based distinguisher ⋮ Modeling large S-box in MILP and a (related-key) differential attack on full round PIPO-64/128

Uses Software

• SIMECK
• SIMON
• SPECK
• deep_speck
• GIFT
• Gimli

Cites Work

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• The block cipher companion.
• MILP-based differential attack on round-reduced GIFT
• Improving attacks on round-reduced Speck32/64 using deep learning
• 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
• A TUTORIAL ON LINEAR AND DIFFERENTIAL CRYPTANALYSIS
• Cryptanalysis of Skipjack Reduced to 31 Rounds Using Impossible Differentials
• Differential and Linear Cryptanalysis Using Mixed-Integer Linear Programming
• GIFT: A Small Present

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