Original title: Can Neural Networks spot the Sponge?
Authors: Gradoš, Matej
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract: Cryptographic hash functions ensure data integrity and security in modern cryptographic systems. They are a core mechanism for digital signatures, authentication protocols, and blockchain technology, designed to make their output impossible to predict. Inspired by machine learning’s ability to approximate complex functions, we trained models of varying complexity to assess whether they can learn and replicate the behavior of cryptographic hash functions. Our study evaluates the extent to which these models are able to approximate the underlying mathematical operations, shedding light on potential vulnerabilities and theoretical limits of machine learning in cryptographic contexts.
Keywords: cryptanalysis; Hash functions; machine learning; neural networks
Host item entry: Proceedings I of the 31st Conference STUDENT EEICT 2025: General papers, ISBN 978-80-214-6321-9

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: https://hdl.handle.net/11012/255267

Permalink: http://www.nusl.cz/ntk/nusl-687776


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