TARU PUBLICATIONS
Journal of Discrete Mathematical Sciences and Cryptography cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0065·ISSN (Print): 0972-0529

Monthly Journal: Publishes theoretical and applied research in all areas of Discrete Mathematical Sciences, Cryptography, Combinatorics, Elliptic Curves and Information Security.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
submissions@tarupublications.com
Open Access Research Article

A discrete graph-theoretic spectral framework for analyzing classical cipher security

*

* Corresponding author · click or hover a name for details

pp. 3305–3313Vol. 29Issue 8August 2026DOI: 10.47974/JDMSC-2817 Crossmark XML
Received:
01 Mar 2026
Published Online:
14 Aug 2026
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-2817
Pages:
3305–3313

Abstract

This paper proposes a spectral graph-theoretic framework for cryptanalysis using Cipher Transition Graphs (CTGs) and metrics such as eigenvalues, spectral gap, entropy, clustering, and path length. Results show that plaintext and Caesar cipher share identical properties (spectral gap 1.2056, entropy 12.6732), indicating weak transformation; Vigenère shows slight variation (gap 1.2652, clustering 0.1545); while Playfair exhibits higher clustering (0.1824) but lower entropy (11.4216). Overall, all ciphers retain structural patterns, confirming their moderate security and limitations.

Keywords

Subject Classifications

Primary 05C50Secondary 94A60

References

[1] F. R. K. Chung, Spectral Graph Theory. Providence, RI, USA: American Mathematical Society (1997).

[2] S. Ji, S. Pan, E. Cambria, P. Marttinen, and P. S. Yu, “A Survey on Knowledge Graphs: Representation, Acquisition, and Applications,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 2, pp. 494–514 (Feb. 2022), doi: 10.1109/TNNLS.2021.3070843.

[3] Y. Banu, B. K. Rath, and D. Gountia, “Analyzing cryptographic algorithm efficiency within graph-based encryption models,” Frontiers in Computer Science, vol. 7, Art. no. 1630222 (2025), doi: 10.3389/fcomp.2025.1630222.

[4] K. Song, N. Imran, J. Y. Chen, and A. C. Dobbins, “A Hybrid Chaos-Based Cryptographic Framework for Post-Quantum Secure Communications,” CoRR, arXiv: abs/2504.08618 (2025).

[5] N. Ali, A. Sadiqa, M. A. Shahzad, M. I. Qureshi, H. M. A. Siddiqui, S. A. O. Abdallah, and N. S. Abd El-Gawaad, “Secure communication in the digital age: A new paradigm with graph-based encryption algorithms,” Frontiers in Computer Science, vol. 6, Art. no. 1454094 (2024), doi: 10.3389/fcomp.2024.1454094.

[6] W. Alexan, K. M. Hosny, and M. Gabr, “A new fast multiple color image encryption algorithm,” Cluster Computing, vol. 28, Art. no. 325 (2025), doi: 10.1007/s10586-024-04919-0.

[7] M. Gabr, D. El-Damak, W. Alexan, M. B. M. Mansour, A. M. Elsayed, M. M. Darwish, and K. M. Hosny, “Fibonacci Q-Matrix, Hyperchaos, and Galois Field (2⁸) for Augmented Medical Image Encryption,” IEEE Access, vol. 12, pp. 102718–102744 (2024), doi: 10.1109/ACCESS.2024.3433499.

[8] M. Martín-Nieto, D. Castaño, S. Horta Muñoz, and D. Ruiz, “Solving Mazes: A New Approach Based on Spectral Graph Theory,” Mathematics, vol. 12, no. 15, Art. no. 2305 (2024), doi: 10.3390/math12152305.

[9] D. Kahrobaei, C. Koupparis, and V. Shpilrain, “Public key exchange using matrices over group rings,” Groups, Complexity, Cryptology, vol. 5, pp. 217–225 (2013).

[10] National Institute of Standards and Technology, “Post-Quantum Cryptography Standardization,” Computer Security Resource Center (CSRC). [Online]. Available: https://csrc.nist.gov/pqc-standardization. [Accessed: Jul. 18, 2026].

[11] A. Noonia, D. Thakral, P. Mathur, F. Sheth, H. Shaikh, and A. K. Gupta, “A discrete mathematical model and cryptography for secure medical image analysis: Encrypted chest X-ray classification,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 28, no. 5-A, pp. 1473–1486 (2025).

[12] D. Goyal, F. Sheth, P. Mathur, and A. K. Gupta, “Discrete mathematical models for enhancing cybersecurity: A mathematical and statistical analysis of machine learning approaches in phishing attack detection,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 2-B, pp. 569–599 (2024).

[13] R. Joshi, P. Mathur, A. K. Gupta, S. Singh, V. Paliwal, and S. Nayar, “Mathematical modeling of intelligent system for predicting effectiveness of premenstrual syndrome,” Journal of Interdisciplinary Mathematics, vol. 26, no. 3, pp. 551–562 (2023).

[14] L. Hogben, “Spectral graph theory and the inverse eigenvalue problem of a graph,” The Electronic Journal of Linear Algebra, vol. 14, pp. 12-31 (2005).

[15] M. E. J. Newman, Networks: An Introduction. Oxford, U.K.: Oxford University Press (2010).

[16] C. E. Shannon, “A mathematical theory of communication,” Bell System Technical Journal, vol. 27, no. 3, pp. 379–423 (1948).

[17] R. Diestel, Graph Theory, 5th ed. Berlin, Germany: Springer (2017).

[18] J. Katz and Y. Lindell, Introduction to Modern Cryptography, 2nd ed. Boca Raton, FL, USA: CRC Press (2014).

[19] U. von Luxburg, “A tutorial on spectral clustering,” Statistics and Computing, vol. 17, no. 4, pp. 395–416 (2007).

Views: 44Downloads: 32Citations: 0