Hybrid feature selection for phishing email detection

Hamid, Isredza Rahmi A. and Abawajy, Jemal 2011, Hybrid feature selection for phishing email detection. In Xiang, Yang, Cuzzocrea, Alfredo, Hobbs, Michael and Zhou, Wanlei (ed), Algorithms and architectures for parallel processing, Springer, Berlin, Germany, pp.266-275.

Attached Files
Name Description MIMEType Size Downloads

Title Hybrid feature selection for phishing email detection
Author(s) Hamid, Isredza Rahmi A.
Abawajy, JemalORCID iD for Abawajy, Jemal orcid.org/0000-0001-8962-1222
Title of book Algorithms and architectures for parallel processing
Editor(s) Xiang, YangORCID iD for Xiang, Yang orcid.org/0000-0001-5252-0831
Cuzzocrea, Alfredo
Hobbs, MichaelORCID iD for Hobbs, Michael orcid.org/0000-0002-7556-6274
Zhou, Wanlei
Publication date 2011
Series Lecture notes in computer science; v. 7017
Chapter number 26
Total chapters 38
Start page 266
End page 275
Total pages 10
Publisher Springer
Place of Publication Berlin, Germany
Keyword(s) behavior-based
feature Selection
internet Security
Summary Phishing emails are more active than ever before and putting the average computer user and organizations at risk of significant data, brand and financial loss. Through an analysis of a number of phishing and ham email collected, this paper focused on fundamental attacker behavior which could be extracted from email header. It also put forward a hybrid feature selection approach based on combination of content-based and behavior-based. The approach could mine the attacker behavior based on email header. On a publicly available test corpus, our hybrid features selections are able to achieve 96% accuracy rate. In addition, we successfully tested the quality of our proposed behavior-based feature using the information gain.
ISBN 9783642246494
ISSN 0302-9743
Language eng
Field of Research 080501 Distributed and Grid Systems
Socio Economic Objective 890206 Internet Hosting Services (incl. Application Hosting Services)
HERDC Research category B1 Book chapter
Copyright notice ©2011, Springer-Verlag Berlin
Persistent URL http://hdl.handle.net/10536/DRO/DU:30043154

Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 18 times in TR Web of Science
Scopus Citation Count Cited 35 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 707 Abstract Views, 29 File Downloads  -  Detailed Statistics
Created: Tue, 13 Mar 2012, 09:48:49 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.