Refined Method of Impossible Differentials Search with Application to Kalyna-Like Ciphers

This work addresses the problem of evaluating the security of block ciphers against impossible differential cryptanalysis, with a particular focus on Kalyna-like ciphers. Based on formalized methods—specifically the Wu-Wang method—this work introduces refined rules tailored to AES- and Kalyna-like ciphers. These refinements simplify compatibility checks by replacing large systems of linear equations with computationally efficient conditions. Experimental results have identified several classes of impossible differentials for three-round versions of certain Kalyna cipher variants, thereby demonstrating the security of full-round ciphers against this method of cryptanalysis.

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Publication Details

Journal
Theoretical and Applied Cybersecurity
Published
2025-08-11
DOI
https://doi.org/10.20535/tacs.2664-29132025.1.332345
Primary Topic
Reinforcement Learning in Robotics
Type
article
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article

Refined Method of Impossible Differentials Search with Application to Kalyna-Like Ciphers

Theoretical and Applied Cybersecurity
Reinforcement Learning in Robotics
article

Refined Method of Impossible Differentials Search with Application to Kalyna-Like Ciphers

article en

Abstract

This work addresses the problem of evaluating the security of block ciphers against impossible differential cryptanalysis, with a particular focus on Kalyna-like ciphers. Based on formalized methods—specifically the Wu-Wang method—this work introduces refined rules tailored to AES- and Kalyna-like ciphers. These refinements simplify compatibility checks by replacing large systems of linear equations with computationally efficient conditions. Experimental results have identified several classes of impossible differentials for three-round versions of certain Kalyna cipher variants, thereby demonstrating the security of full-round ciphers against this method of cryptanalysis.

Theoretical and Applied CybersecurityVol. 7(1)
Institute of History of Ukraine (UA)
Openalex Percentile: Top 26%
Reinforcement Learning in Robotics
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