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Reseach Article

Detection of Phishing Emails using Feed Forward Neural Network

by Noor Ghazi M. Jameel, Loay E. George
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 77 - Number 7
Year of Publication: 2013
Authors: Noor Ghazi M. Jameel, Loay E. George
10.5120/13405-1057

Noor Ghazi M. Jameel, Loay E. George . Detection of Phishing Emails using Feed Forward Neural Network. International Journal of Computer Applications. 77, 7 ( September 2013), 10-15. DOI=10.5120/13405-1057

@article{ 10.5120/13405-1057,
author = { Noor Ghazi M. Jameel, Loay E. George },
title = { Detection of Phishing Emails using Feed Forward Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { September 2013 },
volume = { 77 },
number = { 7 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = { 10-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume77/number7/13405-1057/ },
doi = { 10.5120/13405-1057 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:49:38.403806+05:30
%A Noor Ghazi M. Jameel
%A Loay E. George
%T Detection of Phishing Emails using Feed Forward Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 77
%N 7
%P 10-15
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Phishing emails are messages designed to fool the recipient into handing over personal information, such as login names, passwords, credit card numbers, account credentials, social security numbers etc. Fraudulent emails harm their victims through loss of funds and identity theft. They also hurt Internet business, because people lose their trust in Internet transactions for fear that they will become victims of fraud. This paper deals with the phishing detection problem and how to detect phishing emails. The proposed phishing detection model is based on the extracted email features to detect phishing emails, these features appeared in the header and HTML body of email using feed forward neural network to classify the tested email into phish or ham email. The results of the conducted tests indicated good identification rate (98. 72%) with short required processing time (0. 00067 msec. ).

References
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Index Terms

Computer Science
Information Sciences

Keywords

Phishing Attack Phishing Email Fraud Identity Theft. .