- Title
- Detecting phishing emails using hybrid features
- Creator
- Ma, Liping; Ofoghi, Bahadorreza; Watters, Paul; Brown, Simon
- Date
- 2009
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/61435
- Identifier
- vital:3566
- Identifier
-
https://doi.org/10.1109/UIC-ATC.2009.103
- Identifier
- ISBN:9781424449026
- Abstract
- Phishing emails have been used widely in fraud of financial organizations and customers. Phishing email detection has drawn a lot attention for many researchers and malicious detection devices are installed in email servers. However, phishing has become more and more complicated and sophisticated and attack can bypass the filter set by anti-phishing techniques. In this paper, we present a method to build a robust classifier to detect phishing emails using hybrid features and to select features using information gain. We experiment on 10 cross-validations to build an initial classifier which performs well. The experiment also analyses the quality of each feature using information gain and best feature set is selected after a recursive learning process. Experimental result shows the selected features perform as well as the original features. Finally, we test five machine learning algorithms and compare the performance of each. The result shows that decision tree builds the best classifier.
- Publisher
- Brisbane, Queensland : IEEE Computer Society
- Relation
- Paper presented at 2009 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC '09, Brisbane, Queensland : 7th-9th July 2009 p. 493-497
- Rights
- Copyright IEEE
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- Computer crime; Decision trees; Learning (artificial intelligence); Unsolicited e-mail
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