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 revueArticle

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 originaleAnglais
Numéro d'article012215
Nombre de pages9
journalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume98
Numéro de publication1
Les DOIs
étatPublié - 24 juil. 2018

Empreinte digitale

cryptography
Chaos Synchronization
Chaotic Attractor
Cryptography
chaos
synchronism
Chaotic System
Chaos
machine learning
glass
Lorenz System
Cryptosystem
dynamical systems
Attractor
Machine Learning
Crack
cracks
Dynamical system
Computing

mots-clés

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

Citer ceci

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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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Using a reservoir computer to learn chaotic attractors, with applications to chaos synchronization and cryptography. / Antonik, Piotr; Gulina, Marvyn; Pauwels, Jael ; Massar, Serge.

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 revueArticle

TY - JOUR

T1 - Using a reservoir computer to learn chaotic attractors, with applications to chaos synchronization and cryptography

AU - Antonik, Piotr

AU - Gulina, Marvyn

AU - Pauwels, Jael

AU - Massar, Serge

PY - 2018/7/24

Y1 - 2018/7/24

N2 - 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.

AB - 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.

KW - Ordinateur réservoir

KW - Réseau de neurones récurrents

KW - Réseau d'état d'écho

KW - Cryptographie par chaos

KW - reservoir computer

KW - recurrent neurral network

KW - echo state network

KW - chaos based cryptography

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DO - 10.1103/PhysRevE.98.012215

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JO - Physical Review E - Statistical, Nonlinear, and Soft Matter Physics

JF - Physical Review E - Statistical, Nonlinear, and Soft Matter Physics

SN - 1539-3755

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