Devising a method for improving crypto resistance of the symmetric block cryptosystem RC5 using nonlinear shift functions
DOI:
https://doi.org/10.15587/1729-4061.2021.240344Keywords:
nonlinear function, symmetric cryptosystem, shift function, RC5, block cipher, cryptoanalysisAbstract
This paper analyzes ways to improve the cryptographic strength of the symmetric block cipher RC5. The task to enhance the stability of the classic RC5 cipher is explained by the fact that it is part of various open cryptographic libraries and is frequently used in practice. Several methods have been considered, applying which theoretically contributes to improving the stability of cryptographic transformations. It is found that unlike other alternatives (increasing the number of rounds, the length of the key, and the encryption block), the use of nonlinear shift functions does not increase the computational complexity of the RC5 algorithm. The study result has helped build an analytical model that was implemented in the form of the MATLAB (USA) software application. The software interface provides the ability to manually change the encryption parameters of the RC5 crypto algorithm. The resulting upgrade of the RC5 crypto algorithm has been tested on different sets of input data during encryption and decryption. The resulting modification also does not lead to an increase in the calculation time but makes it possible to improve the resistance to hacking the encrypted data by several orders of magnitude (210), provided that differential analysis methods are used and the number of rounds is 14. For one of the nonlinear functions used, resistance to the differential cryptoanalysis used increased by 212 times already in the eleventh round of encryption. The reliability of the improved cryptosystem has been confirmed by the absence of statistical correlation between the blocks of incoming messages and output blocks, the absence of collisions at which it is possible to obtain the same sequences of bits at the output with different messages at the input. The resulting algorithm could be applied in computer systems with low computing performance
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Copyright (c) 2021 Andrii Sahun, Vladislav Khaidurov, Valeriy Lakhno, Ivan Opirskyy, Vitalii Chubaievskyi, Olena Kryvoruchko, Alona Desiatko
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