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Reseach Article

A Hybrid Model for Classification of E-mail Fraud

by T.O. Oyegoke
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 39
Year of Publication: 2022
Authors: T.O. Oyegoke
10.5120/ijais2022451926

T.O. Oyegoke . A Hybrid Model for Classification of E-mail Fraud. International Journal of Applied Information Systems. 12, 39 ( April 2022), 13-24. DOI=10.5120/ijais2022451926

@article{ 10.5120/ijais2022451926,
author = { T.O. Oyegoke },
title = { A Hybrid Model for Classification of E-mail Fraud },
journal = { International Journal of Applied Information Systems },
issue_date = { April 2022 },
volume = { 12 },
number = { 39 },
month = { April },
year = { 2022 },
issn = { 2249-0868 },
pages = { 13-24 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number39/1126-2022451926/ },
doi = { 10.5120/ijais2022451926 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:11:27.493125+05:30
%A T.O. Oyegoke
%T A Hybrid Model for Classification of E-mail Fraud
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 39
%P 13-24
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The study pre-processed e-mail data, formulated and validated a Particle Swarm Optimization (PSO)-based Back Propagation model for email fraud detection. This was done by the hybridization of two algorithms namely; Nature Inspired Algorithm and Artificial Neural Network. The dataset collected for the purpose of developing the model contained fraudulent mails (46.3%), Spam (32.6%) and Ham (21.1%) e-mails. 12,831 features were extracted after data preparation and cleaning, in which only 6,382 (49.7%) relevant features were selected using PSO. The model was simulated using 70% and 80% for training while 30% and 20% of datasets were used for testing respectively. The results of using the 30% and 20% testing dataset for the gradient-based BP algorithm showed that using the relevant features selected by PSO improved the accuracy by a value of 0.27% and 0.35% respectively while for the PSO-based BP algorithm, using the relevant features selected by PSO improved the accuracy by a value of 1.51% and 1.46% respectively. The results showed that using PSO-based BP had a better performance than gradient-based BP by a value of 1.48% and 2.72% for 30% training dataset and a value of 1.46% and 2.57% using the original features and the features selected using PSO respectively. The study concluded that the PSO-based BP algorithm was able to improve the performance of the Multi-Layer Perceptron compared to the Gradient-Based Back Propagation algorithm which has implications on improving advance fee fraud detection.

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Index Terms

Computer Science
Information Sciences

Keywords

Email Machine Learning Advance Fee Fraud; Fraud detection Artificial Neural Network (ANN) Particle Swarm Optimization (PSO)