Sufficiency of Ensemble Machine Learning Methods for Phishing Websites Detection
Yi Wei, Yuji Sekiya
Abstract
Phishing is a kind of worldwide spread cybercrime that uses disguised websites to trick users into downloading malware or providing personally sensitive information to attackers. With the rapid development of artificial intelligence, more and more researchers in the cybersecurity field utilize machine learning and deep learning algorithms to classify phishing websites. In order to compare the performances of various machine learning and deep learning methods, several experiments are conducted in this study. According to the experimental results, ensemble machine learning algorithms stand out among other candidates in both detection accuracy and computational consumption. Furthermore, the ensemble architectures still provide impressive capability when the amount of features decreases sharply in the dataset. Subsequently, the paper discusses the factors why ensemble machine learning methods are more suitable for the binary phishing classification challenge in up-date training and real-time detecting environment, which reflects the sufficiency of ensemble machine learning methods in anti-phishing techniques.