The Performance of Sequential Deep Learning Models in Detecting Phishing Websites Using Contextual Features of URLs

arXiv:2404.09802v1 Announce Type: new
Abstract: Cyber attacks continue to pose significant threats to individuals and organizations, stealing sensitive data such as personally identifiable information, financial information, and login credentials. Hence, detecting malicious websites before they cause any harm is critical to preventing fraud and monetary loss. To address the increasing number of phishing attacks, protective mechanisms must be highly responsive, adaptive, and scalable. Fortunately, advances in the field of machine learning, coupled with access to vast amounts of data, have …

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