NEURO-FUZZY DATA MINING SYSTEM FOR IDENTIFYING E-COMMERCE RELATED THREATS
Saibu Aliyu Haruna1, Akinyede Raphael Olufemi2 and Boyinbode Olutayo Kehinde3
1Department of Computer Science, The Federal University of Technology, Akure, Nigeria
2Department of Information Systems, The Federal University of Technology, Akure, Nigeria
3Department of Information Technology, The Federal University of Tech., Akure, Nigeria
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.
Keywords: e-Commerce, Adaptive Neuro-Fuzzy Inference System (ANFIS), Electronic Data Interchange (EDI), Information Technology, K-nearest Neighbour (KNN) Algorithm
Published On: 08 September 2020