Comparison between ensemble and linear methods for website phishing detection

Abstract

In the current digitalized world, the notion of cybersecurity has become crucial in everyday life, and the issue of privacy takes the leading role in the technological agenda of the global community. One such social engineering attack that is currently prevalent is phishing, which is a common technique used by cybercriminals to intercept sensitive data. Despite the presence of certain limitations which can restrict its usefulness, machine learning (ML) has evolved into an interesting approach to identify phishing attacks. Cloud ML is an effective solution that uses cloud computing solutions to create, train, and deploy models that provide a faster and more accurate result as well as support large datasets. This paper compares the ensemble method of Amazon SageMaker’s AutoML tool, AutoGluon, with the linear method of SageMaker’s linear learner algorithm for website phishing detection. Key factors examined include training techniques, training time, batch transform time, endpoint prediction time, and model accuracy. The results demonstrate that while AutoGluon outperforms linear learner in terms of accuracy and prediction speed, linear learner is faster in training and batch transform processes.