Neuro-Fuzzy Data Mining System for Identifying E-Commerce Related Threats

Abstract

E-commerce is driven via Information Technology (IT) especially the web it mostly relies upon on innovative technologies that are facilitated by Electronic Data Interchange (EDI) and Electronic Payment over the web. Several researches have shown that e-commerce platforms are compromised by means of phishing and fraud attacks. This has necessitated trying to find innovative methodologies for defending e-commerce systems and users from the said threats. This research integrates Case Based Reasoning (CBR) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to spot and categorise e-commerce websites transactions as legitimate or illegitimate by analyzing and evaluating some attributes. this may provide an invulnerable platforms for e-commerce users. The system, which was implemented on MATLAB, are often deployed on e-commerce systems and servers to watch e-commerce requests with the aim of identifying legitimate and illegitimate websites and transactions. The results of the implementation indicates that the machine is promising.

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“…The Chinese government's establishment of a policy system adapted to the characteristics of cross-border e-commerce reflects the need for sustainable policy frameworks to support the growth of e-commerce (Su et al, 2019). The evolution of ecommerce has also been accompanied by challenges, such as credit problems and the identification of e-commerce-related threats, emphasizing the importance of addressing security and financial issues in the digital marketplace (Haruna et al, 2020;Zhang & Zheng, 2022). Additionally, the readiness of e-commerce to support SMEs in various sectors, including tourism and globalizing business environments, highlights the potential for ecommerce to drive economic growth and competitiveness (Lubis et al, 2019;Savrul et al, 2014).…”