Spying on chaos-based cryptosystems with reservoir computing

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

doi:10.1109/IJCNN.2018.8489102

Spying on chaos-based cryptosystems with reservoir computing

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

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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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.

langue originale	Anglais
titre	2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
Editeur	IEEE
Pages	1-7
Nombre de pages	7
Volume	2018-July
ISBN (Electronique)	978-1-5090-6014-6
ISBN (imprimé)	978-1-5090-6014-6
Les DOIs	https://doi.org/10.1109/IJCNN.2018.8489102
Etat de la publication	Publié - 10 oct. 2018

Série de publications

Nom	2018 International Joint Conference on Neural Networks (IJCNN)

mots-clés

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

Accès au document

10.1109/IJCNN.2018.8489102

AntonikGulina_2017Accepted author manuscript, 47,5 KB

Autres fichiers et liens

4 Citations
1 Article
1 Mémoire de master

Using a reservoir computer to learn chaotic attractors, with applications to chaos synchronization and cryptography
Antonik, P., Gulina, M., Pauwels, J. & Massar, S., 24 juil. 2018, Dans: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics. 98, 1, 9 p., 012215.
Résultats de recherche: Contribution à un journal/une revue › Article › Revue par des pairs

Accès ouvert
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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Contient cette citation

Antonik, P., Gulina, M., Pauwels, J., Rontani, D., Haelterman, M., & Massar, S. (2018). Spying on chaos-based cryptosystems with reservoir computing. Dans 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings (Vol 2018-July, p. 1-7). Article 8489102 (2018 International Joint Conference on Neural Networks (IJCNN)). IEEE. https://doi.org/10.1109/IJCNN.2018.8489102

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title = "Spying on chaos-based cryptosystems with reservoir computing",

abstract = "Reservoir computing is a machine learning approach to designing artificial neuralnetworks. Despite the significant simplification of the training process, theperformance of such systems is comparable to other digital algorithms on a seriesof benchmark tasks. Recent investigations have demonstrated the possibility ofperforming long-horizon predictions of chaotic systems using reservoir computing.In this work we show that a trained reservoir computer can reproduce sufficientlywell the properties a chaotic system, hence allowing full synchronisation. Weillustrate this behaviour on the Mackey-Glass and Lorenz systems. Furthermore, weshow that a reservoir computer can be used to crack chaos-based cryptographicprotocols and illustrate this on two encryption schemes.",

keywords = "Reservoir computer, Recurrent neurral network, Echo state network, Chaos based cryptography, reservoir computing, chaos-based cryptography, eavesdropping, chaos synchronisation, Ordinateur r{\'e}servoir, R{\'e}seau de neurones r{\'e}currents, R{\'e}seau d'{\'e}tat d'{\'e}cho, Cryptographie par chaos",

author = "Piotr Antonik and Marvyn Gulina and Jael Pauwels and Damien Rontani and Marc Haelterman and Serge Massar",

note = "Funding Information: 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 (FRS-FNRS), and 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. P.A. and D.R gratefully acknowledge the support of AFOSR (grants No. FA-9550-15-1-0279 and FA-9550-17-1-0072) and R{\'e}gion Grand-Est. Publisher Copyright: {\textcopyright} 2018 IEEE. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.",

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Antonik, P, Gulina, M, Pauwels, J, Rontani, D, Haelterman, M & Massar, S 2018, Spying on chaos-based cryptosystems with reservoir computing. Dans 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings. VOL. 2018-July, 8489102, 2018 International Joint Conference on Neural Networks (IJCNN), IEEE, p. 1-7. https://doi.org/10.1109/IJCNN.2018.8489102

Spying on chaos-based cryptosystems with reservoir computing. / Antonik, Piotr; Gulina, Marvyn; Pauwels, Jael et al.
2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings. Vol 2018-July IEEE, 2018. p. 1-7 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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AU - Massar, Serge

N1 - Funding Information: 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 (FRS-FNRS), and by the Action de Recherche Concertée of the Fédération Universitaire Wallonie-Bruxelles through Grant No. AUWB-2012-12/17-ULB9. P.A. and D.R gratefully acknowledge the support of AFOSR (grants No. FA-9550-15-1-0279 and FA-9550-17-1-0072) and Région Grand-Est. Publisher Copyright: © 2018 IEEE. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.

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N2 - Reservoir computing is a machine learning approach to designing artificial neuralnetworks. Despite the significant simplification of the training process, theperformance of such systems is comparable to other digital algorithms on a seriesof benchmark tasks. Recent investigations have demonstrated the possibility ofperforming long-horizon predictions of chaotic systems using reservoir computing.In this work we show that a trained reservoir computer can reproduce sufficientlywell the properties a chaotic system, hence allowing full synchronisation. Weillustrate this behaviour on the Mackey-Glass and Lorenz systems. Furthermore, weshow that a reservoir computer can be used to crack chaos-based cryptographicprotocols and illustrate this on two encryption schemes.

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KW - Réseau d'état d'écho

KW - Cryptographie par chaos

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VL - 2018-July

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SP - 1

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BT - 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings

PB - IEEE

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Spying on chaos-based cryptosystems with reservoir computing

Résumé

Série de publications

mots-clés

Accès au document

Autres fichiers et liens

Empreinte digitale

Résultat de recherche

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

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

Contient cette citation