# A Review of Data-driven Approaches for Malicious Website Detection

arXiv: https://arxiv.org/abs/2305.09084 · HTML: https://mlsanity.com/paper/2305.09084/

## 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.

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [XYLayoutLM: Towards Layout-Aware Multimodal Networks For Visually-Rich Document Understanding](https://mlsanity.com/paper/2203.06947/) (2022)
- [URLTran: Improving Phishing URL Detection Using Transformers](https://mlsanity.com/paper/2106.05256/) (2021)
- [Malicious URL Detection using Machine Learning: A Survey](https://mlsanity.com/paper/1701.07179/) (2017)
- [PhishSim: Aiding Phishing Website Detection with a Feature-Free Tool](https://mlsanity.com/paper/2207.10801/) (2022)
- [Less is More: Robust and Novel Features for Malicious Domain Detection](https://mlsanity.com/paper/2006.01449/) (2020)
- [DeepBF: Malicious URL detection using Learned Bloom Filter and Evolutionary Deep Learning](https://mlsanity.com/paper/2103.12544/) (2021)
- [CELEST: Federated Learning for Globally Coordinated Threat Detection](https://mlsanity.com/paper/2205.11459/) (2022)

## 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.