Phishing detection system using nachine learning classifiers

Published on Mar 1, 2020in Indonesian Journal of Electrical Engineering and Computer Science
· DOI :10.11591/IJEECS.V17.I3.PP1165-1171
Nur Sholihah Zaini1
Estimated H-index: 1
(Universiti Malaysia Pahang),
Deris Stiawan10
Estimated H-index: 10
+ 4 AuthorsTole Sutikno17
Estimated H-index: 17
(Universitas Ahmad Dahlan)
Source
Abstract
The increasing development of the Internet, more and more applications are put into websites can be directly accessed through the network. This development has attracted an attacker with phishing websites to compromise computer systems. Several solutions have been proposed to detect a phishing attack. However, there still room for improvement to tackle this phishing threat. This paper aims to investigate and evaluate the effectiveness of machine learning approach in the classification of phishing attack. This paper applied a heuristic approach with machine learning classifier to identify phishing attacks noted in the web site applications. The study compares with five classifiers to find the best machine learning classifiers in detecting phishing attacks. In identifying the phishing attacks, it demonstrates that random forest is able to achieve high detection accuracy with true positive rate value of 94.79% using website features. The results indicate that random forest is effective classifiers for detecting phishing attacks.
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#4Khaled Shaalan (British University in Dubai)H-Index: 36
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In this paper, we present a general scheme for building reproducible and extensible datasets for website phishing detection. The aim is to (1) enable comparison of systems using different features, (2) overtake the short-lived nature of phishing websites, and (3) keep track of the evolution of phishing tactics. For experimenting the proposed scheme, we start by adopting a refined classification of website phishing features and we systematically select a total of 87 commonly recognized ones, we c...
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#1Tsehay Admassu Assegie (Aksum University)H-Index: 3
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