Using a reservoir computer to learn chaotic attractors, with applications to chaos synchronization and cryptography

Piotr Antonik; Marvyn Gulina; Jael  Pauwels; Serge Massar

doi:10.1103/PhysRevE.98.012215

Using a reservoir computer to learn chaotic attractors, with applications to chaos synchronization and cryptography

Piotr Antonik, Marvyn Gulina, Jael Pauwels, Serge Massar

Résultats de recherche: Contribution à un journal/une revue › Article › Revue par des pairs

Résumé

Using the machine learning approach known as reservoir computing, it is possible to train one dynamical system to emulate another. We show that such trained reservoir computers reproduce the properties of the attractor of the chaotic system sufficiently well to exhibit chaos synchronization. That is, the trained reservoir computer, weakly driven by the chaotic system, will synchronize with the chaotic system. Conversely, the chaotic system, weakly driven by a trained reservoir computer, will synchronize with the reservoir computer. We illustrate this behavior on the Mackey-Glass and Lorenz systems. We then show that trained reservoir computers can be used to crack chaos based cryptography and illustrate this on a chaos cryptosystem based on the Mackey-Glass system. We conclude by discussing why reservoir computers are so good at emulating chaotic systems.

langue originale	Anglais
Numéro d'article	012215
Nombre de pages	9
journal	Physical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume	98
Numéro de publication	1
Les DOIs	https://doi.org/10.1103/PhysRevE.98.012215
Etat de la publication	Publié - 24 juil. 2018

mots-clés

Ordinateur réservoir
Réseau de neurones récurrents
Réseau d'état d'écho
Cryptographie par chaos

Accès au document

10.1103/PhysRevE.98.012215

https://arxiv.org/abs/1802.02844

Autres fichiers et liens

Link to publication in Scopus

74 Citations
1 Article dans les actes d'une conférence/un colloque
1 Mémoire de master

Spying on chaos-based cryptosystems with reservoir computing
Antonik, P., Gulina, M., Pauwels, J., Rontani, D., Haelterman, M. & Massar, S., 10 oct. 2018, 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings. IEEE, Vol 2018-July. p. 1-7 7 p. 8489102. (2018 International Joint Conference on Neural Networks (IJCNN)).
Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque

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Utilisation d'un ordinateur réservoir pour décrypter un message transmis via la cryptographie par chaos
Gulina, M., 29 mai 2017, (Non publié)
Résultats de recherche: Thèse externe › Mémoire de master

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On the approximations of the Koopman operator and applications to spectral identification of networks
Auteur: Gulina, M., 3 nov. 2023
Superviseur: Mauroy, A. (Promoteur), Carletti, T. (Copromoteur), Winkin, J. (Président), Hendrickx, J. (Personne externe) (Jury) & Dietrich, F. (Personne externe) (Jury)
Student thesis: Doc types › Docteur en Sciences

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Contient cette citation

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abstract = "Using the machine learning approach known as reservoir computing, it is possible to train one dynamical system to emulate another. We show that such trained reservoir computers reproduce the properties of the attractor of the chaotic system sufficiently well to exhibit chaos synchronization. That is, the trained reservoir computer, weakly driven by the chaotic system, will synchronize with the chaotic system. Conversely, the chaotic system, weakly driven by a trained reservoir computer, will synchronize with the reservoir computer. We illustrate this behavior on the Mackey-Glass and Lorenz systems. We then show that trained reservoir computers can be used to crack chaos based cryptography and illustrate this on a chaos cryptosystem based on the Mackey-Glass system. We conclude by discussing why reservoir computers are so good at emulating chaotic systems.",

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note = "Funding Information: We thank M. Haelterman, G. Verschaffelt, G. Van der Sande, and D. Rontani for helpful discussions. This work was supported by the Interuniversity Attraction Poles Program (Belgian Science Policy) Project Photonics@be IAP P7-35, by the Fonds de la Recherche Scientifique (F.R.S.-FNRS), by the Action de Recherche Concert{\'e}e of the F{\'e}d{\'e}ration Universitaire Wallonie-Bruxelles through Grant No. AUWB-2012-12/17-ULB9, by the Research Foundation–Flanders (FWO, Ph.D. fellowship), and by the Universit{\'e} de Namur. Publisher Copyright: {\textcopyright} 2018 American Physical Society.",

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Using a reservoir computer to learn chaotic attractors, with applications to chaos synchronization and cryptography. / Antonik, Piotr; Gulina, Marvyn; Pauwels, Jael et al.
Dans: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Vol 98, Numéro 1, 012215, 24.07.2018.

Résultats de recherche: Contribution à un journal/une revue › Article › Revue par des pairs

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Using a reservoir computer to learn chaotic attractors, with applications to chaos synchronization and cryptography

Résumé

mots-clés

Accès au document

Autres fichiers et liens

Empreinte digitale

Résultat de recherche

Spying on chaos-based cryptosystems with reservoir computing

Utilisation d'un ordinateur réservoir pour décrypter un message transmis via la cryptographie par chaos

Thèses de l'étudiant

On the approximations of the Koopman operator and applications to spectral identification of networks

Contient cette citation