# DeltaPhish: Detecting Phishing Webpages in Compromised Websites

arXiv: https://arxiv.org/abs/1707.00317 · HTML: https://mlsanity.com/paper/1707.00317/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2017.

## Built on by (most cited first, 13 of 13)

- [The Role of Machine Learning in Cybersecurity](https://mlsanity.com/paper/2206.09707/) (2022)
- [SoK: Explainable Machine Learning for Computer Security Applications](https://mlsanity.com/paper/2208.10605/) (2022)
- [SoK: The Impact of Unlabelled Data in Cyberthreat Detection](https://mlsanity.com/paper/2205.08944/) (2022)
- [Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning](https://mlsanity.com/paper/2210.13660/) (2022)
- [Towards Web Phishing Detection Limitations and Mitigation](https://mlsanity.com/paper/2204.00985/) (2022)
- [Mitigating Adversarial Gray-Box Attacks Against Phishing Detectors](https://mlsanity.com/paper/2212.05380/) (2022)
- [Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors](https://mlsanity.com/paper/2310.03166/) (2023)
- [PhishLang: A Real-Time, Fully Client-Side Phishing Detection Framework Using MobileBERT](https://mlsanity.com/paper/2408.05667/) (2024)
- [PhishClone: Measuring the Efficacy of Cloning Evasion Attacks](https://mlsanity.com/paper/2209.01582/) (2022)
- [ModSec-AdvLearn: Countering Adversarial SQL Injections with Robust Machine Learning](https://mlsanity.com/paper/2308.04964/) (2023)
- [ModSec-Learn: Boosting ModSecurity with Machine Learning](https://mlsanity.com/paper/2406.13547/) (2024)
- [Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Review](https://mlsanity.com/paper/2506.15506/) (2025)
- [Robustness, Cost, and Attack-Surface Concentration in Phishing Detection](https://mlsanity.com/paper/2603.19204/) (2026)

## Abstract (arXiv)

The large-scale deployment of modern phishing attacks relies on the automatic exploitation of vulnerable websites in the wild, to maximize profit while hindering attack traceability, detection and blacklisting. To the best of our knowledge, this is the first work that specifically leverages this adversarial behavior for detection purposes. We show that phishing webpages can be accurately detected by highlighting HTML code and visual differences with respect to other (legitimate) pages hosted within a compromised website. Our system, named DeltaPhish, can be installed as part of a web application firewall, to detect the presence of anomalous content on a website after compromise, and eventually prevent access to it. DeltaPhish is also robust against adversarial attempts in which the HTML code of the phishing page is carefully manipulated to evade detection. We empirically evaluate it on more than 5,500 webpages collected in the wild from compromised websites, showing that it is capable of detecting more than 99% of phishing webpages, while only misclassifying less than 1% of legitimate pages. We further show that the detection rate remains higher than 70% even under very sophisticated attacks carefully designed to evade our system.