A new neural-network-based model for measuring the strength of a pseudorandom binary sequence
Abstract
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.
Cite
@article{arxiv.1910.04195,
title = {A new neural-network-based model for measuring the strength of a pseudorandom binary sequence},
author = {Ahmed Alamer and Ben Soh},
journal= {arXiv preprint arXiv:1910.04195},
year = {2019}
}
Comments
15 pages to be submitted to Logical "Methods in Computer Science" Journal