Hybrid vs Ensemble Classification Models for Phishing Websites

Authors

  • Sakinat Oluwabukonla Folorunso Department of Mathematical Sciences, Olabisi Onabanjo University, Ago-Iwoye, Nigeria
  • Femi Emmanuel Ayo Department of Physical and Computer Sciences, McPherson University, Seriki Sotayo, Nigeria
  • Khadijah-Kuburah Adebisi Abdullah Department of Mathematical Sciences, Olabisi Onabanjo University, Ago-Iwoye, Nigeria
  • Peter Ibikunle Ogunyinka Department of Mathematical Sciences, Olabisi Onabanjo University, Ago-Iwoye, Nigeria

DOI:

https://doi.org/10.24996/ijs.2020.61.12.27

Keywords:

Phishing, Feature selection, Classification, Stacking, Ensemble, Social engineering

Abstract

Phishing is an internet crime achieved by imitating a legitimate website of a host in order to steal confidential information. Many researchers have developed phishing classification models that are limited in real-time and computational efficiency.  This paper presents an ensemble learning model composed of DTree and NBayes, by STACKING method, with DTree as base learner. The aim is to combine the advantages of simplicity and effectiveness of DTree with the lower complexity time of NBayes. The models were integrated and appraised independently for data training and the probabilities of each class were averaged by their accuracy on the trained data through testing process. The present results of the empirical study on phishing website dataset suggest that the ensemble model significantly outperformed the hybrid model in terms of the measures used. Finally, DTree and STACKING methods showed superior performances compared to the other models.

Downloads

Download data is not yet available.

Downloads

Published

2020-12-30

Issue

Section

Computer Science

How to Cite

Hybrid vs Ensemble Classification Models for Phishing Websites. (2020). Iraqi Journal of Science, 61(12), 3387-3396. https://doi.org/10.24996/ijs.2020.61.12.27

Similar Articles

1-10 of 581

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)