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

arXiv:2404.09802v1 Announce Type: cross
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 …

abstract arxiv attacks cs.cr cs.lg cyber cyber attacks data deep learning features financial fraud harm information login organizations performance personally identifiable information phishing stealing threats type urls websites