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Spying on chaos-based cryptosystems with reservoir computing

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. © 2018 IEEE.
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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⟩

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