Open AccessHybrid Machine Learning: A Tool to Detect Phishing Attacks in Communication Networks
Author(s) -
Ademola P. Abidoye,
Boniface Kabaso
Publication year - 2021
Publication title -
ecti transactions on computer and information technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.132
H-Index - 2
ISSN - 2286-9131
DOI - 10.37936/ecti-cit.2021153.240565
Subject(s) - phishing , login , computer science , credit card , computer security , the internet , word error rate , scheme (mathematics) , personally identifiable information , information sensitivity , artificial intelligence , machine learning , world wide web , mathematical analysis , mathematics , payment
Phishing is a cyber-attack that uses disguised email as a weapon and has been on the rise in recent times. Innocent Internet user if peradventure clicking on a fraudulent link may cause him to fall victim of divulging his personal information such as credit card pin, login credentials, banking information and other sensitive information. There are many ways in which the attackers can trick victims to reveal their personal information. In this article, we select important phishing URLs features that can be used by 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 application in detecting phishing URLs.