Phishing Website Detection Using Several Machine Learning Algorithms: A Review Paper

Alexander M. Veach; Munther Abualkibash

doi:10.34010/injiiscom.v3i2.8805

Alexander M. Veach School of Information Security and Applied Computing, Eastern Michigan University, Ypsilanti, Michigan, United States
Munther Abualkibash School of Information Security and Applied Computing, Eastern Michigan University, Ypsilanti, Michigan, United States

DOI: https://doi.org/10.34010/injiiscom.v3i2.8805

Keywords: Artificial Intelligence, Data Science, Machine Learning, Phishing

Abstract

Phishing is one of the major web social engineering attacks. This has led to demand for a better way to predict and stop them in a commercial environment. This paper seeks to understand the research done in the field and analyse the next steps forward. This is done by focusing on what goes into the selection of proper features, from manual selection to the use of Genetic Algorithms such as ADABoost and MultiBoost. Then a look into the classifiers in use, Neural Networks and Ensemble algorithms which were prominent alongside some novel approaches. This information is then processed into a framework for cloud-based and client-based phishing website detection, alongside suggestions for possible future research and experiments that could help progress the field.

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