Abstract
Problem statement: The problem in cryptanalysis can be described as an unknown and the neural networks are ideal tools for black-box syste m identification. In this study, a mathematical bla ck- box model is developed and system identification te chniques are combined with adaptive system techniques, to construct the Neuro-Identifier. Approach: The Neuro-Identifier was discussed as a black-box model to attack the target cipher systems . Results: In this study this model is a new addition in cryptography that presented the methods of block (SDES) crypto systems discussed. The constructing of Neuro-Identifier mode achieved two objectives: The first one was to construct emulator of Neuro-model for the target cipher system, while the second was to (cryptanalysis) determine the key from given plaintext-ciphertext pair. Conclusion: Present the idea of the equivalent cipher system, which is identical 100% to the unknown system and that means that an unknown hardware, or software cipher system could be reconstructed without known the internal circuitry or algorithm of it.
Highlights
Block cipher systems belong to symmetric cryptographic systems, where the same key is used for encryption and decryption process
System identification deals with the problem of building mathematical models of dynamical systems based on observed data from the system (Alallayah et al, 2010; Lennart, 1987)
We describe distinguishing attacks and key-recovery attacks against block ciphers (Ball et al, 2002)
Summary
Block cipher systems belong to symmetric cryptographic systems, where the same key is used for encryption and decryption process. Identification consists of determining the system orders and approximation of the unknown function by neural network model using a set of input and output data (Blankenship and Ghanadan, 1996; Leaster and Sjoberg, 2000; Lester and Jonas, 1998).
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