Phishing Detection Based on Machine Learning and Feature Selection Methods

Mohammad Almseidin, AlMaha Abu Zuraiq, Mouhammd Al-kasassbeh, Nidal Alnidami

Abstract


With increasing technology developments, the Internet has become everywhere and accessible by everyone. There are a considerable number of web-pages with different benefits. Despite this enormous number, not all of these sites are legitimate. There are so-called phishing sites that deceive users into serving their interests. This paper dealt with this problem using machine learning algorithms in addition to employing a novel dataset that related to phishing detection, which contains 5000 legitimate web-pages and 5000 phishing ones. In order to obtain the best results, various machine learning algorithms were tested. Then J48, Random forest, and Multilayer perceptron were chosen. Different feature selection tools were employed to the dataset in order to improve the efficiency of the models. The best result of the experiment achieved by utilizing 20 features out of 48 features and applying it to Random forest algorithm. The accuracy was 98.11%.

Keywords


Phishing Detection, Machine Learning, Feature Selection, Random Forest, Multilayer Perceptron.

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International Journal of Interactive Mobile Technologies (iJIM) – eISSN: 1865-7923
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