Optimizing Fraudulent Transaction Detection In E-Commerce: A Comparative Analysis of Machine Learning And Deep Learning Algorithms With Time And CPU Performance Tracking

Emejuru, Chijioke Franklin (2024) Optimizing Fraudulent Transaction Detection In E-Commerce: A Comparative Analysis of Machine Learning And Deep Learning Algorithms With Time And CPU Performance Tracking. Masters thesis, Dublin, National College of Ireland.

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Abstract

This research presents a concise analysis on the application of machine learning and deep learning techniques for fraudulent detection in e-commerce. With the increasing number of cases of fraudulent activities, many institutions face challenges in detecting these practices in due time. This research evaluates some machine learning techniques such as logistic regression, random forest, support vector machine, decision trees, xgboost, gradient boosting and a deep learning multi-layer perceptron for their effectiveness in the detection. Key findings reveal that Random forest and the ensemble models, with their balance of accuracy and complexity, emerged as the best models with random forest being on top with an accuracy of 99.97% in the detection of fraudulent transactions.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Aleburu, Joel UNSPECIFIED
Uncontrolled Keywords:	Logistic regression; random forest; support vector machine; decision trees; xgboost; gradient boosting and a deep learning multi-layer perceptron; fraudulent detection; machine learning
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security H Social Sciences > HF Commerce > Electronic Commerce Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	18 Jul 2025 11:21
Last Modified:	18 Jul 2025 11:21
URI:	https://norma.ncirl.ie/id/eprint/8203

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