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of the European Council for Modelling and Simulation



Towards Artificial Neural Network Hashing With Strange Attractors Usage


Jacek Tchorzewski, Agnieszka Jakobik

Published in:



2020). ECMS 2020 Proceedings Edited by: Mike Steglich, Christian Muller, Gaby Neumann, Mathias Walther, European Council for Modeling and Simulation.



ISSN: 2522-2422 (ONLINE)

ISSN: 2522-2414 (PRINT)

ISSN: 2522-2430 (CD-ROM)


ISBN: 978-3-937436-68-5
ISBN: 978-3-937436-69-2(CD)


Communications of the ECMS , Volume 34, Issue 1, June 2020,

United Kingdom


Citation format:

Jacek Tchorzewski, Agnieszka Jakobik (2020). Towards Artificial Neural Network Hashing With Strange Attractors Usage, ECMS 2020 Proceedings Edited By: Mike Steglich, Christian Mueller, Gaby Neumann, Mathias Walther European Council for Modeling and Simulation. doi: 10.7148/2020-0354



A broad variety of methods ensuring the integrity of data in the mobile and IoT equipment is very important nowadays. Hash functions are used for detecting the unauthorized modification of data and for digital signatures generation. Traditional hash functions like SHA-2 or SHA-3 have relatively high computational power requirements, therefore are not always suitable (or optimal) for devices with limited computational capacity or battery capacity.

Instead, light cryptography hash functions may be used. They are processing data strings of the shorter length and offers simpler mathematical models as the basis of hash calculation.

In this paper Articial Neural Network (ANN)-based model hashing is proposed. Instead of using s-boxes or complicated compression function, a simple two-layered non-recurrent ANNs are used for hash calculation. In order to provide a very high quality of the randomization of the output, several different chaotic attractors were incorporated into ANNs training phase. ANNs output was tested with appropriate statistical tests and compared with hashes returned by traditional hashing methods. Using shorter hash length enables implementing those methods in the mobile and IoT equipment. Our approach allows merging the low complexity of ANN processing with the high-quality standards of cryptography hash functions.


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