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    Counteracting Dark Web Text-Based CAPTCHA with Generative Adversarial Learning for Proactive Cyber Threat Intelligence

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    Name:
    2201.02799.pdf
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    PDF
    Description:
    Final Accepted Manuscript
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    Author
    Zhang, Ning
    Ebrahimi, Mohammadreza
    Li, Weifeng
    Chen, Hsinchun
    Affiliation
    University of Arizona
    Issue Date
    2022-06-30
    Keywords
    Automated CAPTCHA breaking
    dark web
    generative adversarial networks
    
    Metadata
    Show full item record
    Publisher
    Association for Computing Machinery (ACM)
    Citation
    Zhang, N., Ebrahimi, M., Li, W., & Chen, H. (2022). Counteracting Dark Web Text-Based CAPTCHA with Generative Adversarial Learning for Proactive Cyber Threat Intelligence. ACM Transactions on Management Information Systems.
    Journal
    ACM Transactions on Management Information Systems
    Rights
    © 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Automated monitoring of dark web (DW) platforms on a large scale is the first step toward developing proactive Cyber Threat Intelligence (CTI). While there are efficient methods for collecting data from the surface web, large-scale dark web data collection is often hindered by anti-crawling measures. In particular, text-based CAPTCHA serves as the most prevalent and prohibiting type of these measures in the dark web. Text-based CAPTCHA identifies and blocks automated crawlers by forcing the user to enter a combination of hard-to-recognize alphanumeric characters. In the dark web, CAPTCHA images are meticulously designed with additional background noise and variable character length to prevent automated CAPTCHA breaking. Existing automated CAPTCHA breaking methods have difficulties in overcoming these dark web challenges. As such, solving dark web text-based CAPTCHA has been relying heavily on human involvement, which is labor-intensive and time-consuming. In this study, we propose a novel framework for automated breaking of dark web CAPTCHA to facilitate dark web data collection. This framework encompasses a novel generative method to recognize dark web text-based CAPTCHA with noisy background and variable character length. To eliminate the need for human involvement, the proposed framework utilizes Generative Adversarial Network (GAN) to counteract dark web background noise and leverages an enhanced character segmentation algorithm to handle CAPTCHA images with variable character length. Our proposed framework, DW-GAN, was systematically evaluated on multiple dark web CAPTCHA testbeds. DW-GAN significantly outperformed the state-of-the-art benchmark methods on all datasets, achieving over 94.4% success rate on a carefully collected real-world dark web dataset. We further conducted a case study on an emergent Dark Net Marketplace (DNM) to demonstrate that DW-GAN eliminated human involvement by automatically solving CAPTCHA challenges with no more than three attempts. Our research enables the CTI community to develop advanced, large-scale dark web monitoring. We make DW-GAN code available to the community as an open-source tool in GitHub.
    Note
    Immediate access
    ISSN
    2158-656X
    EISSN
    2158-6578
    DOI
    10.1145/3505226
    Version
    Final accepted manuscript
    Sponsors
    National Science Foundation
    ae974a485f413a2113503eed53cd6c53
    10.1145/3505226
    Scopus Count
    Collections
    UA Faculty Publications

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