Applying the Autoencoder model for URL phishing detection

Abstract

In the era of digital transformation, alongside the rapid development of the Internet and online applications, phishing attacks targeting users through malicious URLs have been increasingly prevalent. Traditional methods for detecting malicious URLs often rely on blacklist-based techniques. However, these techniques have significant limitations as they cannot identify new URLs. Many machine learning-based approaches have been researched and implemented to overcome these shortcomings. This paper proposes an improved two-stage deep learning model using an Autoencoder network for phishing URL detection. The proposed approach will be evaluated and tested on the standard UCI's URL Phishing dataset, achieving better results in most measure metrics.