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.

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}
}