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dc.contributor.authorZhang, Ning
dc.contributor.authorEbrahimi, Mohammadreza
dc.contributor.authorLi, Weifeng
dc.contributor.authorChen, Hsinchun
dc.date.accessioned2022-05-13T21:38:51Z
dc.date.available2022-05-13T21:38:51Z
dc.date.issued2022-06-30
dc.identifier.citationZhang, 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.en_US
dc.identifier.issn2158-656X
dc.identifier.doi10.1145/3505226
dc.identifier.urihttp://hdl.handle.net/10150/664211
dc.description.abstractAutomated 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.en_US
dc.description.sponsorshipNational Science Foundationen_US
dc.language.isoenen_US
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.rights© 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en_US
dc.subjectAutomated CAPTCHA breakingen_US
dc.subjectdark weben_US
dc.subjectgenerative adversarial networksen_US
dc.titleCounteracting Dark Web Text-Based CAPTCHA with Generative Adversarial Learning for Proactive Cyber Threat Intelligenceen_US
dc.typeArticleen_US
dc.identifier.eissn2158-6578
dc.contributor.departmentUniversity of Arizonaen_US
dc.identifier.journalACM Transactions on Management Information Systemsen_US
dc.description.noteImmediate accessen_US
dc.description.collectioninformationThis 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.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.identifier.pii10.1145/3505226
dc.source.journaltitleACM Transactions on Management Information Systems
dc.source.volume13
dc.source.issue2
dc.source.beginpage1
dc.source.endpage21
refterms.dateFOA2022-05-13T21:38:51Z


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