Spying on chaos-based cryptosystems with reservoir computing - Chaire Photonique Accéder directement au contenu
Communication Dans Un Congrès Année : 2018

Spying on chaos-based cryptosystems with reservoir computing

Résumé

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. © 2018 IEEE.
Fichier principal
Vignette du fichier
antonik2018spying_hal-02432576.pdf (1.19 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02432576 , version 1 (28-04-2020)

Identifiants

Citer

Piotr Antonik, Marvyn Gulina, Jaël Pauwels, Damien Rontani, Marc Haelterman, et al.. Spying on chaos-based cryptosystems with reservoir computing. 2018 International Joint Conference on Neural Networks, IJCNN 2018, Jul 2018, Rio de Janeiro, Brazil. pp.8489102, ⟨10.1109/IJCNN.2018.8489102⟩. ⟨hal-02432576⟩
55 Consultations
103 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More