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A Novel Improvement With an Effective Expansion to Enhance the MD5 Hash Function for Verification of a Secure E-Document
MD5 is a one-way cryptographic function used in various fields for maintaining data integrity. The application of a Hash function can provide much protection and privacy and subsequently reduce data usage. Most users are familiar with validating electronic documents based on a Hash function, such as...
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Published in: | IEEE access 2020, Vol.8, p.80290-80304 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | MD5 is a one-way cryptographic function used in various fields for maintaining data integrity. The application of a Hash function can provide much protection and privacy and subsequently reduce data usage. Most users are familiar with validating electronic documents based on a Hash function, such as the MD5 algorithm and other hash functions, to demonstrate the data integrity. There are many weaknesses of the current MD5 algorithm, mainly its failures and weaknesses against varying types of attacks, such as brute force attacks, rainbow table attacks, and Christmas attacks. Therefore, the method proposed in this paper enhances the MD5 algorithm by adding a dynamic variable length and a high efficiency that simulates the highest security available. Whereas the logistic system was used to encode ribonucleic acid (RNA) by generating a random matrix based on a new key that was created using the initial permutation (IP) tables used in the data encryption stander (DES) with the linear-feedback shift register (LFSR), this work proposes several structures to improve the MD5 hash function. The experimental results demonstrate its high resistance to hackers while maintaining a suitable duration. This paper discusses the design of a confident hash algorithm. This algorithm has characteristics that enable it to succeed in the field of digital authentication and data integrity. |
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ISSN: | 2169-3536 2169-3536 |
DOI: | 10.1109/ACCESS.2020.2989050 |