The Performance of Sequential Deep Learning Models in Detecting Phishing
  Websites Using Contextual Features of URLs
Saroj Gopali Akbar S. Namin Faranak Abri Keith S. Jones
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 led to the adoption of various deep learning models
for timely detection of these cyber crimes. This study focuses on the detection
of phishing websites using deep learning models such as Multi-Head Attention,
Temporal Convolutional Network (TCN), BI-LSTM, and LSTM where URLs of the
phishing websites are treated as a sequence. The results demonstrate that
Multi-Head Attention and BI-LSTM model outperform some other deep
learning-based algorithms such as TCN and LSTM in producing better precision,
recall, and F1-scores.