A new neural-network-based model for measuring the strength of a pseudorandom binary sequence

TL;DR

This paper introduces a neural network model to efficiently and accurately measure the strength of pseudorandom binary sequences by predicting their maximum order complexity, outperforming classical methods.

Contribution

It presents a novel neural-network-based approach for predicting maximum order complexity of pseudorandom sequences, improving accuracy and efficiency over traditional techniques.

Findings

Neural network model outperforms classical prediction methods.

Using UWS as a complexity measure enhances prediction accuracy.

Model demonstrates efficiency in measuring sequence strength.

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.