  The detection of malicious websites has become a critical issue in
cybersecurity. Therefore, this paper offers a comprehensive review of
data-driven methods for detecting malicious websites. Traditional approaches
and their limitations are discussed, followed by an overview of data-driven
approaches. The paper establishes the data-feature-model-extension pipeline and
the latest research developments of data-driven approaches, including data
preprocessing, feature extraction, model construction and technology extension.
Specifically, this paper compares methods using deep learning models proposed
in recent years. Furthermore, the paper follows the
data-feature-model-extension pipeline to discuss the challenges together with
some future directions of data-driven methods in malicious website detection.
