Neural Stringology Based Cryptanalysis of EChaCha20

TL;DR

This paper introduces a Neural Stringology Cryptanalysis framework that combines string pattern analysis and machine learning to detect subtle structural anomalies in stream cipher keystreams, exemplified on EChaCha20.

Contribution

It presents a novel hybrid approach integrating stringology and neural networks for cryptanalysis, revealing potential structural weaknesses in ARX-based stream ciphers.

Findings

NSC can identify distinguishable structural patterns in keystreams

The framework detects anomalies in reduced round variants of EChaCha20

Combining stringology with machine learning enhances cryptanalysis capabilities

Abstract

Modern stream ciphers rely on strong diffusion and pseudorandom keystream generation (PKG) to resist cryptanalysis. While conventional evaluation methods such as statistical randomness tests and differential analysis provide important security assurances, they may fail to detect localized structural patterns embedded within cipher outputs. In this paper, a Neural Stringology Cryptanalysis (NSC) framework that combines classical string pattern analysis with machine learning techniques to investigate potential structural anomalies in stream cipher keystreams is introduced. The proposed approach first applies stringology-inspired feature extraction methods such as m-gram frequency analysis, substring recurrence detection, and positional pattern statistics aligned with the internal operations of Add-Rotate-XOR (ARX) based stream ciphers. These extracted features are then analyzed using a neural learning model to identify deviations from expected random behavior and to detect subtle structural patterns that may not be captured by traditional statistical tests. Experimental evaluation is conducted on keystream outputs generated by the EChaCha20 stream cipher under multiple configurations, including reduced round variants. The results demonstrate that the proposed NSC framework can identify distinguishable structural characteristics in the keystream data under controlled conditions, suggesting that integrating machine learning with stringology-based analysis provides a promising complementary methodology for evaluating the structural robustness of modern ARX-based stream cipher designs.