mlsanityData to 14 Oct 2026

A Review of Data-driven Approaches for Malicious Website Detection

arXiv 2305.09084 ↗First posted 16 May 2023cs.CR cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 65 references that are in the map. First posted on arXiv 16 May 2023.

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References · 14

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  5. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  6. 2020Longformer: The Long-Document Transformercited by 3,413
  7. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,310
  8. 2022XYLayoutLM: Towards Layout-Aware Multimodal Networks For Visually-Rich Document Understandingcited by 68
  9. 2021URLTran: Improving Phishing URL Detection Using Transformerscited by 18
  10. 2017Malicious URL Detection using Machine Learning: A Surveycited by 15
  11. 2022PhishSim: Aiding Phishing Website Detection with a Feature-Free Toolcited by 5
  12. 2020Less is More: Robust and Novel Features for Malicious Domain Detectioncited by 4
2 more
  1. 2021DeepBF: Malicious URL detection using Learned Bloom Filter and Evolutionary Deep Learningcited by 4
  2. 2022CELEST: Federated Learning for Globally Coordinated Threat Detectioncited by 3

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