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This paper introduces Neural Stringology Cryptanalysis (NSC), a framework that combines string pattern analysis with machine learning to detect structural anomalies in stream cipher keystreams. NSC extracts features like m-gram frequencies and substring recurrences, informed by the internal operations of ARX ciphers, and then uses a neural network to identify deviations from randomness. Experiments on EChaCha20 keystreams show that NSC can identify structural characteristics not captured by traditional statistical tests, suggesting it can complement existing cryptanalysis methods.
Machine learning can reveal weaknesses in modern stream ciphers that traditional statistical tests miss.
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.
