Phishing detection system using nachine learning classifiers
Nur Sholihah Zaini, Deris Stiawan, Mohd Faizal Ab Razak, Ahmad Qolfathiriyus Firdaus, Wan Isni Sofiah Wan Din, Shahreen binti Kasim, Tole Sutikno · Indonesian Journal of Electrical Engineering and Computer Science · 2020
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.