Spying on chaos-based cryptosystems with reservoir computing

Piotr Antonik, Marvyn Gulina, Jael Pauwels, Damien Rontani, Marc Haelterman, Serge Massar

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

Abstract

Reservoir computing is a machine learning approach to designing artificial neural
networks. Despite the significant simplification of the training process, the
performance of such systems is comparable to other digital algorithms on a series
of benchmark tasks. Recent investigations have demonstrated the possibility of
performing long-horizon predictions of chaotic systems using reservoir computing.
In this work we show that a trained reservoir computer can reproduce sufficiently
well the properties a chaotic system, hence allowing full synchronisation. We
illustrate this behaviour on the Mackey-Glass and Lorenz systems. Furthermore, we
show that a reservoir computer can be used to crack chaos-based cryptographic
protocols and illustrate this on two encryption schemes.
Original languageEnglish
Title of host publication2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PublisherIEEE
Pages1-7
Number of pages7
Volume2018-July
ISBN (Electronic)978-1-5090-6014-6
ISBN (Print)978-1-5090-6014-6
DOIs
Publication statusPublished - 10 Oct 2018

Publication series

Name2018 International Joint Conference on Neural Networks (IJCNN)

Keywords

  • Reservoir computer
  • Recurrent neurral network
  • Echo state network
  • Chaos based cryptography
  • reservoir computing
  • chaos-based cryptography
  • eavesdropping
  • chaos synchronisation

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