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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/6931
Title: Enhancing the security and performance of Business-to-Business E-Commerce using Hybrid Model
Authors: SIME, YISHAK
Issue Date: Jan-2022
Publisher: ST. MARY’S UNIVERSITY
Abstract: Electronic commerce is a modern platform which allows buyer and seller to transact on line through an electronic wire without travelling long distance through crossing boundaries. It enable the sellers to penetrate the huge global market on providing goods and services to the potential buyers and initiate the buyer to purchase goods and services. Electronic commerce cannot be realized without strong security procedure which guarantee the buyer and seller that they are transacting in safe environment which unauthorized user do not interfere their communication. The primary essence of this thesis is to create a more reliable and efficient security environment for the electronic market in general and specifically for the Business-to-Business kind of commerce. Various researches are made e-commerce security using the Symmetric and Asymmetric cryptographic algorithm, but only a few papers were done on reducing the execution time of algorithm without compromising the strength of security. This thesis were done to fill the gap on reducing the execution time of the AES algorithm from 10 iteration round to 8. There were no papers done this aspect and this thesis use the standard AES algorithm and reduced the number of iteration from 10 to 8 and to avoid the limitation of AES algorithm on key exchange between the sender and receiver by decrypting the encryption key using the MD5 hashing algorithm to ensure more security. This thesis were employed experimental research methods and measured the execution time of the standard AES algorithm and the MRRA algorithm on selected sample files of Text, PDF and Audio files of 100KB and 1MB using Java cryptoutil and the result indicates that the MRRA reduced the execution time of the standard AES algorithm by 4%.
URI: .
http://hdl.handle.net/123456789/6931
Appears in Collections:Master of computer science

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