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Performance of Computing Hash-Codes with Chaotically-Trained Artificial Neural Networks

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13351)


The main goal of the research presented in this paper was to estimate the performance of applying neural networks trained with the usage of a chaotic model, that may serve as hashing functions. The Lorenz Attractor chaotic model was used for training data preparation, and Scaled Conjugate Gradient was used as a training algorithm. Networks consisted of two layers: a hidden layer with sigmoid neurons and an output layer with linear neurons. The method of bonding the input message with chaotic formula is presented. Created networks could return 256 or 512 bits of hash, however, this parameter can be easily adjusted before the training process. The performance analysis of networks is discussed (that is the time of hash computation) in comparison with popular standards SHA-256 and SHA-512 under the MATLAB environment. Further research may include analysis of networks’ training parameters (like mean squared error or gradient) or analysis of results of the statistical tests performed on networks output. The presented solution may be used as a security algorithm complementary to a certificated one (for example for additional data integrity checking).


  • Hashing algorithm
  • Artificial Neural Networks
  • Scalable cryptography algorithm
  • Hashing efficiency

Supported by the funds assigned by The Polish Ministry of Education and Science to Cracow University of Technology (J.T.) and to AGH University of Science and Technology (A.B.).

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  • DOI: 10.1007/978-3-031-08754-7_48
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Tchórzewski, J., Byrski, A. (2022). Performance of Computing Hash-Codes with Chaotically-Trained Artificial Neural Networks. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13351. Springer, Cham.

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