Phishing email detection technique by using hybrid features

Email provides convenience of communicating to such large number of people, especially for businessman. However, more attacks are launched to target electronic communication user in order to harvest credentials information from them for illegal purpose used. The most commonly phishing method is init...

Full description

Saved in:
Bibliographic Details
Main Author: Lew, May Form
Format: Final Year Project Report
Language:English
English
Published: Universiti Malaysia Sarawak, (UNIMAS) 2014
Subjects:
Online Access:http://ir.unimas.my/id/eprint/39079/1/Lew%20May%20Form%2024pgs.pdf
http://ir.unimas.my/id/eprint/39079/4/Lew%20May%20ft.pdf
http://ir.unimas.my/id/eprint/39079/
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Universiti Malaysia Sarawak
Language: English
English
id my.unimas.ir.39079
record_format eprints
spelling my.unimas.ir.390792024-03-05T04:13:32Z http://ir.unimas.my/id/eprint/39079/ Phishing email detection technique by using hybrid features Lew, May Form L Education (General) QA Mathematics Email provides convenience of communicating to such large number of people, especially for businessman. However, more attacks are launched to target electronic communication user in order to harvest credentials information from them for illegal purpose used. The most commonly phishing method is initialed by sending out email to user tends to make the user believe that they are communicating with trusted enttify, and deceive them into providing personal information. Recently, there are a lot of research have been done to overcome the phishing emails problem. This project aim to design a phishing email detection technique and focus on feature selection. The proposed method contains content-based feature. URL-based feature and behavior-based feature, which total nine feature sets. The proposed method has been evaluated on a set of 500 phishing emails and 500 legitimate emails. The proposed method obtain overall occuracy 97.25% with 1% false negative rate and 5% false positive rate. The proposed method able to classify more occurotely than the hybrid feature proposed by Hamid et al.. This evidence that two newly add on feature sets, hyperlink feature and return path feature are potential indicator. The quite promising result is motivated future work to mine the attacker behavior and explore more about behavior-based feature. Universiti Malaysia Sarawak, (UNIMAS) 2014 Final Year Project Report NonPeerReviewed text en http://ir.unimas.my/id/eprint/39079/1/Lew%20May%20Form%2024pgs.pdf text en http://ir.unimas.my/id/eprint/39079/4/Lew%20May%20ft.pdf Lew, May Form (2014) Phishing email detection technique by using hybrid features. [Final Year Project Report] (Unpublished)
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
English
topic L Education (General)
QA Mathematics
spellingShingle L Education (General)
QA Mathematics
Lew, May Form
Phishing email detection technique by using hybrid features
description Email provides convenience of communicating to such large number of people, especially for businessman. However, more attacks are launched to target electronic communication user in order to harvest credentials information from them for illegal purpose used. The most commonly phishing method is initialed by sending out email to user tends to make the user believe that they are communicating with trusted enttify, and deceive them into providing personal information. Recently, there are a lot of research have been done to overcome the phishing emails problem. This project aim to design a phishing email detection technique and focus on feature selection. The proposed method contains content-based feature. URL-based feature and behavior-based feature, which total nine feature sets. The proposed method has been evaluated on a set of 500 phishing emails and 500 legitimate emails. The proposed method obtain overall occuracy 97.25% with 1% false negative rate and 5% false positive rate. The proposed method able to classify more occurotely than the hybrid feature proposed by Hamid et al.. This evidence that two newly add on feature sets, hyperlink feature and return path feature are potential indicator. The quite promising result is motivated future work to mine the attacker behavior and explore more about behavior-based feature.
format Final Year Project Report
author Lew, May Form
author_facet Lew, May Form
author_sort Lew, May Form
title Phishing email detection technique by using hybrid features
title_short Phishing email detection technique by using hybrid features
title_full Phishing email detection technique by using hybrid features
title_fullStr Phishing email detection technique by using hybrid features
title_full_unstemmed Phishing email detection technique by using hybrid features
title_sort phishing email detection technique by using hybrid features
publisher Universiti Malaysia Sarawak, (UNIMAS)
publishDate 2014
url http://ir.unimas.my/id/eprint/39079/1/Lew%20May%20Form%2024pgs.pdf
http://ir.unimas.my/id/eprint/39079/4/Lew%20May%20ft.pdf
http://ir.unimas.my/id/eprint/39079/
_version_ 1793163077506564096