A SECURITY MODEL FOR PREVENTING E-COMMERCE RELATED CRIMES

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ABSTRACT

The major challenge being faced by the financial related institutions, such as e-Commerce has been insecurity. Therefore, there is urgent need to develop a scheme to protect transmitted financial information or messages from getting to the third party, intruder and/or unauthorized person(s). Such scheme will be based on Advanced Encryption Standard (AES) and Neural Data Security (NDS) Model. Based on this background, an AES using Time-based Dynamic Key Generation coupled with NDS model will be used to develop security model for preventing e-commerce related crimes. While AES will secure users’ details in the database server and ensures login authentications, NDS model will fragment or partition sensitive data into High and Low levels of confidentiality. The sensitivity of the data will determine, which category of confidentiality the data will fall into. The fragmented data are saved into two different databases, on two different servers and on the same datacenter. In addition, an exploratory survey was carried out using different performance metrics with different classifications of algorithms. Out of the four algorithms considered, Naive Bayes performs better as it shows, out of a total of 105 instances that were observed, 85.71% were correctly classified while 14.29% were misclassified.

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Akinyede, R. O., Adegbenro, S. O., & Omilodi, B. M. (2020). A security model for preventing e-Commerce related crimes. Applied Computer Science, 16(3), 30-41. doi:10.23743/acs-2020-19
Akinyede, Raphael Olufemi, Sulaiman Omolade Adegbenro, and Babatola Moses Omilodi. "A Security Model for Preventing E-Commerce Related Crimes." Applied Computer Science 16, no. 3 (2020): 30-41.
R. O. Akinyede, S. O. Adegbenro, and B. M. Omilodi, "A security model for preventing e-Commerce related crimes," Applied Computer Science, vol. 16, no. 3, pp. 30-41, 2020, doi: 10.23743/acs-2020-19.
Akinyede RO, Adegbenro SO, Omilodi BM. A security model for preventing e-Commerce related crimes. Applied Computer Science. 2020;16(3):30-41.