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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
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Open Access Research Article

A cyber-physical system with blockchain integrated chaotic secure hash algorithms-256 for secured supply chain management system

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pp. 1935–1944Vol. 28Issue 5-BAugust 2025DOI: 10.47974/JDMSC-2369 Crossmark XML
Received:
05 Nov 2024
Published Online:
30 Aug 2025
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-2369
Pages:
1935–1944

Abstract

In the background of the contemporary Supply Chain (SC) environment, the motivation is on cyber security of Cyber-Physical Systems (CPS), openness, and optimization. This paper aims to develop a Secured Supply Chain Management System (SSMS) that combines Blockchain with Chaotic Secure Hash Algortihn-256 (CPS + BC + Chaotic SHA-256) encryption. BC is a distributed and secure database that can be used to track the measure of goods in real-time and with digital certification. Chaotic SHA-256 is used to enhance security by integrating chaotic systems into the SHA-256 Hash Function (HF), thereby increasing the layers of unpredictability and security against attacks. Besides improving the quality of transaction records, this method also provides the best defense against data theft and fraud. The developed CPS + BC + Chaotic SHA-256 has the potential to overcome most of the problems that affect the SC, such as counterfeits and organizational inefficiencies, by utilizing Smart Contracts (SC). The combination of BC in and the Chaotic SHA-256 (C- SHA-256) algorithm generally enhances security and SC stability.

Keywords

Subject Classifications

93E1094A13

References

[1] S. Saberi, M. Kouhizadeh, J. Sarkis and L. Shen, “Blockchain technology and its relationships to sustainable supply chain management,” International Journal of Production Research, vol. 57, no. 7, p. 2117-2135, (2019).
[2] M. M. Queiroz, R. Telles and S. H. Bonilla, “Blockchain and supply chain management integration: a systematic review of the literature,” Supply Chain Management: An International Journal, vol. 25, no. 2, p. 241-254, (2020).
[3] A. Gurtu and J. Johny, “Potential of blockchain technology in supply chain management: a literature review,” International Journal of Physical Distribution & Logistics Management, vol. 49, no. 9, p. 881-900, (2019).
[4] R. Cole, M. Stevenson and J. Aitken, “Blockchain technology: implications for operations and supply chain management,” Supply Chain Management: An International Journal, vol. 24, no. 4, p. 469-483, (2019).
[5] S. Vijayarani,V. J. Tharini and C. Sivamathi, “Business intelligence for evaluating the intangible benefits of on-shelf high utility itemset from the temporal transaction database,” International Journal of Engineering and Advanced Technology, vol. 8, no. 6, (2019).
[6] G. Narayanan, I. Cvitić, D. Peraković and S. P. Raja, “Role of blockchain technology in supply chain management,” IEEE Access, (2024).
[7] M. A. Ghanimi, Hayder, P. Melgarejo-Bolivar, Romel, A. Tumi-Figueroa, S. Ray, D. Pankaj and S. Sudhakar, “Merkle-Damgå RD hash functions and blockchains: Securing electronic health records,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 2-A, p. 237–248, (2024).
[8] M. A. Ghanimi, Hayder, T. Gopalakrishnan, D. G. Joel Sunny, K. Amarendra, D. Pankaj and S.  Sudhakar, “Chebyshev polynomial approximation in CNN for zero-knowledge encrypted data analysis,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 2-A, p. 203–214, (2024). 
[9] P. Vidya Sagar, M. A. Hayder, L. Prabhu Arokia Jesu Raja, D. Pankaj and S. Sudhakar, “Secure multi-party computation in deep learning: Enhancing privacy in distributed neural networks,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 2-A, p. 249–259, (2024).
[10] G. R. K. Rao, M. A. Ghanimi, Hayder, V. Ramachandran, D. Al-Qahtani, D. Pankaj and S. Sudhakar, “Enhanced security in federated learning by integrating homomorphic encryption for privacy-protected, collaborative model training,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 2-A, p. 361–370, (2024).
[11] K. R. Jaya, T. Gopalakrishnan, M. Divyapushpalakshmi, K. Amarendra, D. Pankaj, S. Sudhakar, “Security and privacy concerns in social networks mathematically modified metaheuristic-based approach,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no, 2-A, p. 371–382, (2024).

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