# Malicious URL Detection using Machine Learning: A Survey

arXiv: https://arxiv.org/abs/1701.07179 · HTML: https://mlsanity.com/paper/1701.07179/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2017.

## Built on by (most cited first, 15 of 15)

- [Phishing URL Detection: A Network-based Approach Robust to Evasion](https://mlsanity.com/paper/2209.01454/) (2022)
- [Transformers for End-to-End InfoSec Tasks: A Feasibility Study](https://mlsanity.com/paper/2212.02666/) (2022)
- [LLMs are One-Shot URL Classifiers and Explainers](https://mlsanity.com/paper/2409.14306/) (2024)
- [Towards Web Phishing Detection Limitations and Mitigation](https://mlsanity.com/paper/2204.00985/) (2022)
- [An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework](https://mlsanity.com/paper/2204.13172/) (2022)
- [DomURLs_BERT: Pre-trained BERT-based Model for Malicious Domains and URLs Detection and Classification](https://mlsanity.com/paper/2409.09143/) (2024)
- [CELEST: Federated Learning for Globally Coordinated Threat Detection](https://mlsanity.com/paper/2205.11459/) (2022)
- [Continuous Multi-Task Pre-training for Malicious URL Detection and Webpage Classification](https://mlsanity.com/paper/2402.11495/) (2024)
- [Identifying malicious accounts in Blockchains using Domain Names and associated temporal properties](https://mlsanity.com/paper/2106.13420/) (2021)
- [Detection of Malicious Websites Using Machine Learning Techniques](https://mlsanity.com/paper/2209.09630/) (2022)
- [Detecting Phishing Sites -- An Overview](https://mlsanity.com/paper/2103.12739/) (2021)
- [From Past to Present: A Survey of Malicious URL Detection Techniques, Datasets and Code Repositories](https://mlsanity.com/paper/2504.16449/) (2025)
- [WebGuard++:Interpretable Malicious URL Detection via Bidirectional Fusion of HTML Subgraphs and Multi-Scale Convolutional BERT](https://mlsanity.com/paper/2506.19356/) (2025)
- [A Review of Data-driven Approaches for Malicious Website Detection](https://mlsanity.com/paper/2305.09084/) (2023)
- [A Graph-Attentive LSTM Model for Malicious URL Detection](https://mlsanity.com/paper/2510.15971/) (2025)

## Abstract (arXiv)

Malicious URL, a.k.a. malicious website, is a common and serious threat to cybersecurity. Malicious URLs host unsolicited content (spam, phishing, drive-by exploits, etc.) and lure unsuspecting users to become victims of scams (monetary loss, theft of private information, and malware installation), and cause losses of billions of dollars every year. It is imperative to detect and act on such threats in a timely manner. Traditionally, this detection is done mostly through the usage of blacklists. However, blacklists cannot be exhaustive, and lack the ability to detect newly generated malicious URLs. To improve the generality of malicious URL detectors, machine learning techniques have been explored with increasing attention in recent years. This article aims to provide a comprehensive survey and a structural understanding of Malicious URL Detection techniques using machine learning. We present the formal formulation of Malicious URL Detection as a machine learning task, and categorize and review the contributions of literature studies that addresses different dimensions of this problem (feature representation, algorithm design, etc.). Further, this article provides a timely and comprehensive survey for a range of different audiences, not only for machine learning researchers and engineers in academia, but also for professionals and practitioners in cybersecurity industry, to help them understand the state of the art and facilitate their own research and practical applications. We also discuss practical issues in system design, open research challenges, and point out some important directions for future research.