Generative Adverserial Analysis of Phishing Attacks on Static and Dynamic Content of Webpages

Alexander O'Mara, Izzat Alsmadi, Ahmed Aleroud

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    In this paper, we studied the phishing problem from data analytics and AI-powered adversarial attacks perspectives. We evaluated several static and dynamic features that can be used as good models' predictors. Unlike the URL, which can be easily crafted to evade detection, the page static and dynamic content cannot be easily changed without changing what is presented to the potential victim. We evaluated several conventional and ensemble-based models and reported the best models and settings of models that showed high prediction accuracy. We then analyzed the feasibility of evading phishing classifiers by perturbing static and dynamic features using AI generative models then test both conventional and ensemble classifiers. Our results shows that the analysis of static and dynamic features of web pages has good potential in the area adversarial learning to generate phishing attacks. The results yield that it is more challenging to evade phishing classifiers relying on dynamic content features, which offers a good level of robustness against evasion tactics.

    Original languageEnglish (US)
    Title of host publication19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1657-1662
    Number of pages6
    ISBN (Electronic)9781665435741
    DOIs
    StatePublished - 2021
    Event19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021 - New York, United States
    Duration: Sep 30 2021Oct 3 2021

    Publication series

    Name19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

    Conference

    Conference19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021
    Country/TerritoryUnited States
    CityNew York
    Period9/30/2110/3/21

    Keywords

    • Dynamic Features
    • Features
    • Machine Learning
    • Phishing
    • Social engineering
    • URL

    ASJC Scopus subject areas

    • Communication
    • Artificial Intelligence
    • Computer Networks and Communications
    • Computer Science Applications
    • Hardware and Architecture
    • Information Systems
    • Information Systems and Management
    • Renewable Energy, Sustainability and the Environment

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