Malicious URLs persistently threaten the cybersecurity ecosystem, by either
deceiving users into divulging private data or distributing harmful payloads to
infiltrate host systems. Gaining timely insights into the current state of this
ongoing battle holds significant importance. However, existing reviews exhibit
4 critical gaps: 1) Their reliance on algorithm-centric taxonomies obscures
understanding of how detection approaches exploit specific modal information
channels; 2) They fail to incorporate pivotal LLM/Transformer-based defenses;
3) No open-source implementations are collected to facilitate benchmarking; 4)
Insufficient dataset coverage.This paper presents a comprehensive review of
malicious URL detection technologies, systematically analyzing methods from
traditional blacklisting to advanced deep learning approaches (e.g.
Transformer, GNNs, and LLMs). Unlike prior surveys, we propose a novel
modality-based taxonomy that categorizes existing works according to their
primary data modalities (URL, HTML, Visual, etc.). This hierarchical
classification enables both rigorous technical analysis and clear understanding
of multimodal information utilization. Furthermore, to establish a profile of
accessible datasets and address the lack of standardized benchmarking (where
current studies often lack proper baseline comparisons), we curate and analyze:
1) publicly available datasets (2016-2024), and 2) open-source implementations
from published works(2013-2025). Then, we outline essential design principles
and architectural frameworks for product-level implementations. The review
concludes by examining emerging challenges and proposing actionable directions
for future research. We maintain a GitHub repository for ongoing curating
datasets and open-source implementations:
https://github.com/sevenolu7/Malicious-URL-Detection-Open-Source/tree/master.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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