An Overview of Hash Function Cryptanalysis Methods
DOI:
https://doi.org/10.24160/1993-6982-2025-5-146-152Keywords:
hashing function, cryptanalysis, collision, neuron, compression functionsAbstract
Nowadays, we are witnessing rapid development of digitalization and network technologies, which intensely use cryptographic algorithms like ciphers, hash functions, and digital signatures. To ensure information security, it is important to use strong cryptographic primitives. Their strength is determined by cryptanalysis. The article addresses methods for cryptographic analysis of hash functions. The aim of the study is to develop a method for analyzing cryptographic hash functions based on neural networks. To this end, the following tasks were set forth:
- defining target requirements for cryptographic hash functions for which cryptographic analysis using artificial neural networks (ANN) is possible;
- searching for an algorithm for analyzing hash functions using ANN;
- determining the ANN type for analyzing cryptographic hash functions.
The existing methods for analyzing iterative cryptographic hash functions are classified. A method for analyzing hash functions using neural networks is proposed. The type of artificial neural networks suitable for performing hash function analysis is determined. A method for evaluating the strength of cryptographic hash functions using neural networks is described. The application field of the study results obtained is quite extensive in nature. It includes both the use of ANN to analyze specific hashing functions for resistance to finding a preimage, resistance to searching for collisions of the first and second kind, and the comparison of several hash functions with each other. The use of neural networks in the analysis of hash functions will make it possible to bring the computer analysis of hash functions closer to a cryptanalysis conducted by qualified specialists in the field of information security. Existing methods for assessing the resistance of hash functions to various attacks are based on mathematical principles that may be far from the "real" resistance of a cryptographic primitive. Thus, artificial neural networks provide a different approach to analyzing cryptographic hash functions.
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Для цитирования: Борисов А.О., Епишкина А.В. Обзор методов криптоанализа хеш-функций // Вестник МЭИ. 2025. № 5. С. 146—152. DOI: 10.24160/1993-6982-2025-5-146-152
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Конфликт интересов: авторы заявляют об отсутствии конфликта интересов
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For citation: Borisov A.O., Epishkina A.V. An Overview of Hash Function Cryptanalysis Methods. Bulletin of MPEI. 2025;5:146—152. (in Russian). DOI: 10.24160/1993-6982-2025-5-146-152
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Conflict of interests: the authors declare no conflict of interest
