Bypassing Detection of URL-based Phishing Attacks Using Generative Adversarial Deep Neural Networks

Author:

AlEroud Ahmed1,Karabatis George2

Affiliation:

1. Yarmouk University, Irbid, Jordan

2. University of Maryland, Baltimore County, Halethorpe, MD, USA

Publisher

ACM

Reference37 articles.

1. Howarth F. The role of human error in successful security attacks. (2014). Retrieved on 20/10/2019 from https://securityintelligence.com/the-role-of-human-error-in-successful-security-attacks Howarth F. The role of human error in successful security attacks. (2014). Retrieved on 20/10/2019 from https://securityintelligence.com/the-role-of-human-error-in-successful-security-attacks

2. Phish Phactors: Offensive and Defensive Strategies

3. PhishGuard: A browser plug-in for protection from phishing

4. A stacking model using URL and HTML features for phishing webpage detection

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1. Investigation of Phishing Susceptibility with Explainable Artificial Intelligence;Future Internet;2024-01-17

2. PhishVision: A Deep Learning Based Visual Brand Impersonation Detector for Identifying Phishing Attacks;Communications in Computer and Information Science;2024

3. Multi-SpacePhish: Extending the Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning;Digital Threats: Research and Practice;2023-12-20

4. Detecting Tabnabbing Attacks Via An RL-Based Agent;2023 8th International Conference on Information Technology Research (ICITR);2023-12-07

5. Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors;Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security;2023-11-26

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