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ECRIME
2007

A comparison of machine learning techniques for phishing detection

9 years 2 months ago
A comparison of machine learning techniques for phishing detection
There are many applications available for phishing detection. However, unlike predicting spam, there are only few studies that compare machine learning techniques in predicting phishing. The present study compares the predictive accuracy of several machine learning methods including Logistic Regression (LR), Classification and Regression Trees (CART), Bayesian Additive Regression Trees (BART), Support Vector Machines (SVM), Random Forests (RF), and Neural Networks (NNet) for predicting phishing emails. A data set of 2889 phishing and legitimate emails is used in the comparative study. In addition, 43 features are used to train and test the classifiers. Keywords BART, CART, classification, logistic regression, machine learning, NNet, phishing, random forests, SVM
Saeed Abu-Nimeh, Dario Nappa, Xinlei Wang, Suku Na
Added 14 Aug 2010
Updated 14 Aug 2010
Type Conference
Year 2007
Where ECRIME
Authors Saeed Abu-Nimeh, Dario Nappa, Xinlei Wang, Suku Nair
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