mlsanityData to 14 Oct 2026

From Past to Present: A Survey of Malicious URL Detection Techniques, Datasets and Code Repositories

arXiv 2504.16449 ↗First posted 23 Apr 2025cs.CR cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 32 of its 273 references that are in the map. First posted on arXiv 23 Apr 2025.

Builds on

32 of 273 references.

References · 32

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2018Online Learning: A Comprehensive Surveycited by 205
  4. 2018URLNet: Learning a URL Representation with Deep Learning for Malicious URL Detectioncited by 34
  5. 2017eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keyscited by 18
  6. 2021URLTran: Improving Phishing URL Detection Using Transformerscited by 18
  7. 2020Advanced Evasion Attacks and Mitigations on Practical ML-Based Phishing Website Classifierscited by 16
  8. 2017Malicious URL Detection using Machine Learning: A Surveycited by 15
  9. 2024Vul-LMGNNs: Fusing language models and online-distilled graph neural networks for code vulnerability detectioncited by 11
  10. 2023PMANet: Malicious URL detection via post-trained language model guided multi-level feature attention networkcited by 10
  11. 2020Look Before You Leap: Detecting Phishing Web Pages by Exploiting Raw URL And HTML Characteristicscited by 9
  12. 2022Toward More Generalized Malicious URL Detection Modelscited by 9
20 more
  1. 2021Enhancing Unsupervised Anomaly Detection with Score-Guided Networkcited by 8
  2. 2022TSGN: Transaction Subgraph Networks Assisting Phishing Detection in Ethereumcited by 8
  3. 2024Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learningcited by 8
  4. 2022Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learningcited by 7
  5. 2024LLMs are One-Shot URL Classifiers and Explainerscited by 6
  6. 2024APOLLO: A GPT-based tool to detect phishing emails and generate explanations that warn userscited by 6
  7. 2019Using Lexical Features for Malicious URL Detection -- A Machine Learning Approachcited by 5
  8. 2022An Adversarial Attack Analysis on Malicious Advertisement URL Detection Frameworkcited by 5
  9. 2023TransURL: Improving malicious URL detection with multi-layer Transformer encoding and multi-scale pyramid featurescited by 5
  10. 2024DomURLs_BERT: Pre-trained BERT-based Model for Malicious Domains and URLs Detection and Classificationcited by 5
  11. 2013PhishAri: Automatic Realtime Phishing Detection on Twittercited by 4
  12. 2020Less is More: Robust and Novel Features for Malicious Domain Detectioncited by 4
  13. 2014An Evasion and Counter-Evasion Study in Malicious Websites Detectioncited by 3
  14. 2021Semi-supervised Conditional GAN for Simultaneous Generation and Detection of Phishing URLs: A Game theoretic Perspectivecited by 3
  15. 2024Continuous Multi-Task Pre-training for Malicious URL Detection and Webpage Classificationcited by 3
  16. 2021Classification of URL bitstreams using Bag of Bytescited by 2
  17. 2023Fed-urlBERT: Client-side Lightweight Federated Transformers for URL Threat Analysiscited by 2
  18. 2024Phishing Website Detection through Multi-Model Analysis of HTML Contentcited by 2
  19. 2024AntiPhishStack: LSTM-based Stacked Generalization Model for Optimized Phishing URL Detectioncited by 1
  20. 2025Efficient Phishing URL Detection Using Graph-based Machine Learning and Loopy Belief Propagationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Benchmarking Large Language Models for Zero-shot and Few-shot Phishing URL Detectioncited by 0