Phishing,SMishing,andVishing·Chapter6 159 Applying Machine Learning for Phishing Detection Machine learning involves building computer applications that can learn and improve from experience. However, unlike predicting spam, only a few studies have used machine learning techniques to predict phishing. A distributed client-server architecture can be applied to conceal the overhead caused by machine learning techniques, albeit take advantage of their high predictive accuracy. The distributed client-server framework exploits the competitive predictive accuracy of machine learning approaches and feeds it to other classifiers running on resource-constrained devices. In the literature, there exist several machine learning techniques for binary classification-- that is, classifiers that assign instances into two groups of data. For example, spam or phishing prediction is a binary classification problem since e-mails are either classified as legitimate or phishing based according to certain characteristics. Such techniques include logistic regression, neural networks (NNet), binary trees and their derivatives, discriminate analysis (DA), Bayesian networks (BN), nearest neighbor (NN), support vector machines (SVM), boosting, bagging, and others. In what follows, we briefly provide an overview of some of these classifiers and illustrate how they can be used to detect phishing e-mails.