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Journal of Applied Mathematics
Volume 2014, Article ID 425731, 6 pages
http://dx.doi.org/10.1155/2014/425731
Research Article

Classification of Phishing Email Using Random Forest Machine Learning Technique

School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Private Bag Box X54001, Durban 4000, South Africa

Received 23 January 2014; Accepted 11 March 2014; Published 3 April 2014

Academic Editor: Olabisi Falowo

Copyright © 2014 Andronicus A. Akinyelu and Aderemi O. Adewumi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved phishing email classifier with better prediction accuracy and fewer numbers of features. From a dataset consisting of 2000 phishing and ham emails, a set of prominent phishing email features (identified from the literature) were extracted and used by the machine learning algorithm with a resulting classification accuracy of 99.7% and low false negative (FN) and false positive (FP) rates.