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Stringology Based Cryptology

arXiv Security Archived Apr 21, 2026 ✓ Full text saved

arXiv:2604.16669v1 Announce Type: new Abstract: The modern cryptographic primitives are known to generate large volumes of sequential data like keystreams, ciphertext blocks, and hash outputs. Traditional cryptgraphic evaluation methods rely primarily on statistical randomness tests and algebraic cryptanalysis techniques. This paper introduces the concept of Stringology-Based Cryptology (SBC), which applies classical string processing and pattern matching techniques to analyze structural propert

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    Computer Science > Cryptography and Security [Submitted on 17 Apr 2026] Stringology Based Cryptology Victor Kebande The modern cryptographic primitives are known to generate large volumes of sequential data like keystreams, ciphertext blocks, and hash outputs. Traditional cryptgraphic evaluation methods rely primarily on statistical randomness tests and algebraic cryptanalysis techniques. This paper introduces the concept of Stringology-Based Cryptology (SBC), which applies classical string processing and pattern matching techniques to analyze structural properties of cryptographic outputs. By interpreting cryptographic outputs as symbolic sequences, stringology algorithms can be used to detect pattern recurrence, substring distributions, and structural correlations. In addition, the paper demonstrate how pattern frequency analysis and substring recurrence metrics can be applied to evaluate keystream outputs generated by stream ciphers. Experimental results illustrate that SBC analysis provides complementary insights into structural characteristics of cryptographic sequences and may support future research in structural cryptanalysis and cryptographic evaluation Comments: 6 pages, 4 figures, accepted for publication at the 2nd International Conference on Sustainability, Innovation and Society (ICSIS 2026), Valencia, Spain Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2604.16669 [cs.CR]   (or arXiv:2604.16669v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.16669 Focus to learn more Submission history From: Victor Kebande [view email] [v1] Fri, 17 Apr 2026 19:58:56 UTC (1,194 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Apr 21, 2026
    Archived
    Apr 21, 2026
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