Abstract

This study presents a method of secure key generation based on fractional-order chaotic systems and Echo State Networks (ESNs). In this case, the time series data of a chaotic system is generated from the fractional order Lorenz system using the Adams–Bashforth–Moulton method that solves the system numerically. An ESN is built with a fixed random reservoir that is trained using one state variable as the input and another as the target output. Afterwards, the output obtained is processed into a binary stream, which is then used for cryptographic key generation. Those keys are integrated into the AES encryption system. There exists a real-time interaction with the chaotic data and the encryption bleeps through a GUI which is MATLAB based. Simulations confirm the initial expectation that the ESN can model the chaotic behavior and provide fragmented keys while still satisfying the criteria for security and randomness. The approach developed here with regards to the proposal is to combine elements of chaos theory and fractional computation with machine learning for implementing advanced cryptographic systems.

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