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Volume 12, Issue 5, May (2022)                               Cover Page and Table of Contents

S.No Title & Authors Full Text
1 Hybrid Framework Architecture for Mitigating Vulnerability Attacks on E-Commerce Using Machine Learning
Martin Doe, Christian Bakaweri Baatuomu, Michael Asante
Abstract - The use of e-commerce as a new trend for making a business accessible to anyone interested in starting their own company is on the rise. Its advantages are so great that people are spending a lot of money on Ecommerce platforms to improve the quality of their business, retain clients, and please them. The recent wave of cyber-attacks against e-commerce platforms and websites, on the other hand, presents new challenges. Existing e-commerce platforms have flaws in their structure, making them open to many types of assaults. As a result, this research devised and implemented a safe hybrid framework to combat persistent e-commerce platform threats. The architecture includes an SVM machine learning component for identifying and rejecting suspicious e-commerce system attacks, which solves vulnerability attacks. The suggested e-commerce architecture outperforms the existing ones in terms of the Open Web Application Security Project, according to performance evaluations.
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