Neuro-fuzzy data mining system for identifying e-commerce related threats / Saibu Aliyu Haruna, Akinyede Raphael Olufemi and Boyinbode Olutayo Kehinde - Haruna, Saibu Aliyu Haruna; Olufemi, Akinyede Raphael; Kehinde, Boyinbode Olutayo (Universiti Teknologi MARA, 2020)
E-commerce is driven via Information Technology (IT), especially the web, and 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 the importance of trying to find innovative methodologies for protecting e-commerce systems and users from the said threats. This research integrates Case Based Reasoning Module (CBRM) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to spot and categorise e-commerce websites transactions as legitimate or illegitimate by analysing and evaluating some attributes. This may provide an invulnerable platform for e-commerce users. The system which was implemented on MATLAB can be deployed on e-commerce systems and servers to watch e-commerce requests with the aim to identify legitimate and illegitimate websites and transactions. The result of the implementation indicates that the developed system is promising.