A new neural-network-based model for measuring the strength of a pseudorandom binary sequence
Maximum order complexity is an important tool for measuring the nonlinearity of a pseudorandom sequence. There is a lack of tools for predicting the strength of a pseudorandom binary sequence in an effective and efficient manner. To this end, this paper proposes a neural-network-based model for measuring the strength of a pseudorandom binary sequence. Using the Shrinking Generator (SG) keystream as pseudorandom binary sequences, then calculating the Unique Window Size (UWS) as a representation of Maximum order complexity, we demonstrate that the proposed model provides more accurate and efficient predictions (measurements) than a classical method for predicting the maximum order complexity.
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