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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Wan Isni Sofiah Wan Din
University Malaysia Pahang

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Phishing detection system using machine learning classifiers Nur Sholihah Zaini; Deris Stiawan; Mohd Faizal Ab Razak; Ahmad Firdaus; Wan Isni Sofiah Wan Din; Shahreen Kasim; Tole Sutikno
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 3: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i3.pp1165-1171

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.
Co-Authors Ahmad Firdaus Deris Stiawan Mohd Faizal Ab Razak Nur Sholihah Zaini Shahreen Kasim Tole Sutikno