Finding the differential characteristics of block ciphers with neural networks

DOI10.1016/j.ins.2008.02.016zbMath1283.94047OpenAlexW2149552539WikidataQ121435089 ScholiaQ121435089MaRDI QIDQ933659

Babak Sadeghiyan, Reza Safabakhsh, Abbas Ghaemi Bafghi

Publication date: 24 July 2008

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2008.02.016

zbMATH Keywords

simulated annealingblock cipherdifferential cryptanalysisBoltzmann machinechaotic Boltzmann machinechaotic Hopfielddifferential operation modelhopfield network

Mathematics Subject Classification ID

Cryptography (94A60)

Related Items (5)

Approximation capabilities of multilayer fuzzy neural networks on the set of fuzzy-valued functions ⋮ Approximation of fuzzy-valued functions by regular fuzzy neural networks and the accuracy analysis ⋮ Passive learning and input-to-state stability of switched Hopfield neural networks with time-delay ⋮ A unified method for finding impossible differentials of block cipher structures ⋮ Security analysis of the public key algorithm based on Chebyshev polynomials over the integer ring \(Z_{N}\)

Uses Software

• Serpent

Cites Work

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• Finding an internal state of RC4 stream cipher
• Attacks of simple block ciphers via efficient heuristics
• ``Neural computation of decisions in optimization problems
• OWA trees and their role in security modeling using attack trees
• Serpent: A New Block Cipher Proposal
• Neural networks and physical systems with emergent collective computational abilities.

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