DeltaPhish: Detecting Phishing Webpages in Compromised Websites
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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.
Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Jul 2017.
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Citing papers per year: 2022: 7; 2023: 2; 2024: 2; 2025: 1; 2026: 1.
13 papers.
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- 2022The Role of Machine Learning in Cybersecuritycited by 22
- 2022SoK: Explainable Machine Learning for Computer Security Applicationscited by 20
- 2022SoK: The Impact of Unlabelled Data in Cyberthreat Detectioncited by 15
- 2022Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learningcited by 7
- 2022Towards Web Phishing Detection Limitations and Mitigationcited by 5
- 2022Mitigating Adversarial Gray-Box Attacks Against Phishing Detectorscited by 5
- 2023Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectorscited by 3
- 2024PhishLang: A Real-Time, Fully Client-Side Phishing Detection Framework Using MobileBERTcited by 3
- 2022PhishClone: Measuring the Efficacy of Cloning Evasion Attackscited by 1
- 2023ModSec-AdvLearn: Countering Adversarial SQL Injections with Robust Machine Learningcited by 1
- 2024ModSec-Learn: Boosting ModSecurity with Machine Learningcited by 1
- 2025Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Reviewcited by 0
1 more
- 2026Robustness, Cost, and Attack-Surface Concentration in Phishing Detection19 Mar 2026
- 2025Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Review18 Jun 2025
- 2024PhishLang: A Real-Time, Fully Client-Side Phishing Detection Framework Using MobileBERT11 Aug 2024
- 2024ModSec-Learn: Boosting ModSecurity with Machine Learning19 Jun 2024
- 2023Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors4 Oct 2023
- 2023ModSec-AdvLearn: Countering Adversarial SQL Injections with Robust Machine Learning9 Aug 2023
- 2022Mitigating Adversarial Gray-Box Attacks Against Phishing Detectors11 Dec 2022
- 2022Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning24 Oct 2022
- 2022PhishClone: Measuring the Efficacy of Cloning Evasion Attacks4 Sep 2022
- 2022SoK: Explainable Machine Learning for Computer Security Applications22 Aug 2022
- 2022The Role of Machine Learning in Cybersecurity20 Jun 2022
- 2022SoK: The Impact of Unlabelled Data in Cyberthreat Detection18 May 2022
1 more
- 2022The Role of Machine Learning in Cybersecuritycited by 22
- 2022SoK: Explainable Machine Learning for Computer Security Applicationscited by 20
- 2022SoK: The Impact of Unlabelled Data in Cyberthreat Detectioncited by 15
- 2022Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learningcited by 7
- 2022Towards Web Phishing Detection Limitations and Mitigationcited by 5
- 2022Mitigating Adversarial Gray-Box Attacks Against Phishing Detectorscited by 5
- 2023Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectorscited by 3
- 2024PhishLang: A Real-Time, Fully Client-Side Phishing Detection Framework Using MobileBERTcited by 3
- 2022PhishClone: Measuring the Efficacy of Cloning Evasion Attackscited by 1
- 2023ModSec-AdvLearn: Countering Adversarial SQL Injections with Robust Machine Learningcited by 1
- 2024ModSec-Learn: Boosting ModSecurity with Machine Learningcited by 1
- 2025Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Reviewcited by 0
12 most cited of 13 All 13 (JSON)
- 2026Robustness, Cost, and Attack-Surface Concentration in Phishing Detection19 Mar 2026
- 2025Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Review18 Jun 2025
- 2024PhishLang: A Real-Time, Fully Client-Side Phishing Detection Framework Using MobileBERT11 Aug 2024
- 2024ModSec-Learn: Boosting ModSecurity with Machine Learning19 Jun 2024
- 2023Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors4 Oct 2023
- 2023ModSec-AdvLearn: Countering Adversarial SQL Injections with Robust Machine Learning9 Aug 2023
- 2022Mitigating Adversarial Gray-Box Attacks Against Phishing Detectors11 Dec 2022
- 2022Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning24 Oct 2022
- 2022PhishClone: Measuring the Efficacy of Cloning Evasion Attacks4 Sep 2022
- 2022SoK: Explainable Machine Learning for Computer Security Applications22 Aug 2022
- 2022The Role of Machine Learning in Cybersecurity20 Jun 2022
- 2022SoK: The Impact of Unlabelled Data in Cyberthreat Detection18 May 2022
12 newest of 13 All 13 (JSON)