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Enhanced Security in Supply Chain Management System Using AES and Md5 Algorithms.

Authors :
Mohamed, S. Raja
Rajendran, N.
Ali, I. Sathik
Kabeer, M.
Source :
Journal of Pharmaceutical Negative Results; 2022 Special Issue, Vol. 13, p78-84, 7p
Publication Year :
2022

Abstract

A supply chain is an order of activities engaged which circulates, assembles and handles the products to move the benefits from a dealer under the control of the last customer. It is an interconnected compound network controlled by supply and demand. Cyber security in SCM is one of the segment of its estimates of protection which primarily gives attention in managing the essential virtual protection which comprises of system software of information technology. In the existing system, cloud services must need extra applications and assistances to locate, govern and protect data which initiate extra supply chain contributors. The manufacturing process data will mislead the manufacturing process in this system based on errors which are done manually. To Store and Maintain data in a protected manner, most algorithms such as DES (Data Encryption Standard) algorithm has disadvantages and threats which seems to be an upper hand for the hackers who are working to steal the data all around. In this paper, for securing the private and secret data, we applied and executed AES (Advanced Encryption Standard) algorithm and MD5 (Message-Digest algorithm 5) in supply chain management. We apply PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyzes) approach in domain of supply chain management, data security, and cyber security to screen the various methods and algorithms which are published in various journal papers and to select a unique and best approach to be used in supply chain management and its security. This method is basically a kind of literature survey to select a best topic for doing a project or a research paper. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09769234
Volume :
13
Database :
Complementary Index
Journal :
Journal of Pharmaceutical Negative Results
Publication Type :
Academic Journal
Accession number :
160271097
Full Text :
https://doi.org/10.47750/pnr.2022.13.S03.014