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.
CITATION STYLE
Antonik, P., Gulina, M., Pauwels, J., Rontani, D., Haelterman, M., & Massar, S. (2018). Spying on chaos-based cryptosystems with reservoir computing. In Proceedings of the International Joint Conference on Neural Networks (Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/IJCNN.2018.8489102
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