Phishing Detection Based on Machine Learning and Feature Selection Methods

Phishing Detection Based on Machine Learning and Feature Selection Methods

Phishing Detection

Machine Learning

Feature Selection

Random Forest

Multilayer Perceptron.

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%.

Almseidin, Mohammad

Abu Zuraiq, AlMaha

Al-kasassbeh, Mouhammd

Alnidami, Nidal

International Journal of Interactive Mobile Technologies (iJIM); Vol. 13 No. 12 (2019); pp. 171-183

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2019-12-18

Copyright (c) 2019 Mohammad Almseidin

application/pdf

eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article