Phishing web site detection using diverse machine learning algorithms

Abstract

Purpose

This paper aims to present a framework to detect phishing websites using stacking model. Phishing is a type of fraud to access users’ credentials. The attackers access users’ personal and sensitive information for monetary purposes. Phishing affects diverse fields, such as e-commerce, online business, banking and digital marketing, and is ordinarily carried out by sending spam emails and developing identical websites resembling the original websites. As people surf the targeted website, the phishers hijack their personal information.

Design/methodology/approach

Features of phishing data set are analysed by using feature selection techniques including information gain, gain ratio, Relief-F and recursive feature elimination (RFE) for feature selection. Two features are proposed combining the strongest and weakest attributes. Principal component analysis with diverse machine learning algorithms including (random forest [RF], neural network [NN], bagging, support vector machine, Naïve Bayes and k-nearest neighbour) is applied on proposed and remaining features. Afterwards, two stacking models: Stacking1 (RF + NN + Bagging) and Stacking2 (kNN + RF + Bagging) are applied by combining highest scoring classifiers to improve the classification accuracy.

Findings

The proposed features played an important role in improving the accuracy of all the classifiers. The results show that RFE plays an important role to remove the least important feature from the data set. Furthermore, Stacking1 (RF + NN + Bagging) outperformed all other classifiers in terms of classification accuracy to detect phishing website with 97.4% accuracy.

Originality/value

This research is novel in this regard that no previous research focusses on using feed forward NN and ensemble learners for detecting phishing websites.

Citation

Zamir, A., Khan, H.U., Iqbal, T., Yousaf, N., Aslam, F., Anjum, A. and Hamdani, M. (2020), "Phishing web site detection using diverse machine learning algorithms", The Electronic Library, Vol. 38 No. 1, pp. 65-80. https://doi.org/10.1108/EL-05-2019-0118