Using Feature Selection and Classification Scheme for Automating Phishing Email Detection

Isredza Rahmi A HAMID¹, Jemal ABAWAJY¹, Tai-hoon KIM²
¹School of Information Technology, Deakin University,
Waurn Ponds, VIC., 3217, Australia,
iraha@deakin.edu.au, Jemal@deakin.edu.au
²School of Computing and Information Science, University of Tasmania,
Centenary Building, room 350, Private Bag 87 Hobart TAS 7001
(Corresponding Author)
taihoonn@empas.com

Abstract: Email has become the critical communication medium for most organizations. Unfortunately, email-born attacks in computer networks are causing considerable economic losses worldwide. Exiting phishing email blocking appliances have little effect in weeding out the vast majority of phishing emails. At the same time, online criminals are becoming more dangerous and sophisticated. 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. In this paper, we propose a hybrid feature selection approach based on combination of content-based and behaviour-based. The approach could mine the attacker behaviour based on email header. On a publicly available test corpus, our hybrid features selection is able to achieve 94% accuracy rate.

Keywords: Internet Security, Behavior-based, Feature Selection, Phishing.

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CITE THIS PAPER AS:
Isredza Rahmi A HAMID, Jemal ABAWAJY, Tai-hoon KIM, Using Feature Selection and Classification Scheme for Automating Phishing Email Detection, Studies in Informatics and Control, ISSN 1220-1766, vol. 22 (1), pp. 61-70, 2013. https://doi.org/10.24846/v22i2y101307