The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Metadata Harvesting (OAI2)
  • Digital Archiving Policy

IJACSA

  • About the Journal
  • Call for Papers
  • Author Guidelines
  • Fees/ APC
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Editors
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving
  • Editorial Board

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors

Computing Conference 2021

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys) 2021

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC) 2021

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future of Information and Communication Conference (FICC) 2021

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Guidelines
  • Fees
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Editors
  • Reviewers
  • Subscribe

Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Hybrid Machine Learning: A Tool to Detect Phishing Attacks in Communication Networks

Author 1: Ademola Philip Abidoye
Author 2: Boniface Kabaso

Download PDF

Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110668

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 6, 2020.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Phishing is a cyber-attack that uses disguised email as a weapon and has been on the rise in recent times. Innocent Internet users if peradventure clicking on a fraudulent link may cause him to fall victim to divulging his personal information such as credit card PIN, login credentials, banking information, and other sensitive information. There are many ways in which attackers can trick victims to reveal their personal information. In this article, we select important phishing URLs features that can be used by an attacker to trick Internet users into taking the attacker's desired action. We use two machine learning techniques to accurately classify our data sets. We compare the performance of other related techniques with our scheme. The results of the experiments show that the approach is highly effective in detecting phishing URLs and attained an accuracy of 97.8% with 1.06% false-positive rate, 0.5% false-negative rate, and an error rate of 0.3%. The proposed scheme performs better compared to other selected related work. This shows that our approach can be used for real-time applications in detecting phishing URLs.

Keywords: Phishing attack; data sets; URL classification; phishing URL; attackers; machine learning; classifiers; Internet

Ademola Philip Abidoye and Boniface Kabaso, “Hybrid Machine Learning: A Tool to Detect Phishing Attacks in Communication Networks” International Journal of Advanced Computer Science and Applications(IJACSA), 11(6), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110668

@article{Abidoye2020,
title = {Hybrid Machine Learning: A Tool to Detect Phishing Attacks in Communication Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110668},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110668},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {6},
author = {Ademola Philip Abidoye and Boniface Kabaso}
}


IJACSA

Upcoming Conferences

Future of Information and Communication Conference (FICC) 2021

29-30 April 2021

  • Virtual

Computing Conference 2021

15-16 July 2021

  • London, United Kingdom

IntelliSys 2021

2-3 September 2021

  • Amsterdam, The Netherlands

Future Technologies Conference (FTC) 2021

28-29 October 2021

  • Vancouver, Canada
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© 2018 The Science and Information (SAI) Organization Limited. Registered in England and Wales. Company Number 8933205. All rights reserved. thesai.org