Attack of against simplified data encryption standard cipher system using neural networks

Abstract

Problem statement: The problem in cryptanalysis can be described as an unknown and the neural networks are ideal tools for black-box system identification. In this study, a mathematical blackbox model is developed and system identification techniques 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. © 2010 Science Publications.