A new neural-network-based model for measuring the strength of a pseudorandom binary sequence
This work addresses a lack of effective tools for measuring pseudorandom sequence strength, which is important for cryptography and security applications, but it appears incremental as it builds on existing concepts like Unique Window Size.
The paper tackled the problem of predicting the strength of pseudorandom binary sequences by proposing a neural-network-based model, which demonstrated more accurate and efficient predictions compared to a classical method for maximum order complexity.
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.