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dc.contributor.advisorChen, Hsinchunen
dc.contributor.authorBenjamin, Victor
dc.creatorBenjamin, Victoren
dc.date.accessioned2016-06-15T19:11:34Z
dc.date.available2016-06-15T19:11:34Z
dc.date.issued2016
dc.identifier.urihttp://hdl.handle.net/10150/613280
dc.description.abstractCybersecurity has become one of the most pressing issues facing society today. In particular, cybercriminals often congregate within online communities to exchange knowledge and assets. As a result, there has been a strong interest in recent years in developing a deeper understanding on cybercriminal behaviors, the global cybercriminal supply chain, emerging threats, and various other cybersecurity-related activities. However, few works in recent years have focused on identifying, collecting, and analyzing cybercriminal contents. Despite the high societal impact of cybercriminal community research, only a few studies have leveraged these rich data sources in their totality, and those that do often resort to manual data collection and analysis techniques. In this dissertation, I address two broad research questions: 1) In what ways can I advance cybersecurity as a science by scrutinizing the contents of online cybercriminal communities? and 2) How can I make use of computational methodologies to identify, collect, and analyze cybercriminal communities in an automated and scalable manner? To these ends, the dissertation comprises four essays. The first essay introduces a set of computational methodologies and research guidelines for conducting cybercriminal community research. To this point, there has been no literature establishing a clear route for non-technical and non-security researchers to begin studying such communities. The second essay examines possible motives for prolonged participation by individuals within cybercriminal communities. The third essay develops new neural network language model (NNLM) capabilities and applies them to cybercriminal community data in order to understand hacker-specific language evolution and to identify emerging threats. The last essay focuses on developing a NNLM-based framework for identifying information dissemination among varying international cybercriminal populations by examining multilingual cybercriminal forums. These essays help further establish cybersecurity as a science.
dc.language.isoen_USen
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en
dc.subjectCybersecurityen
dc.subjectForumsen
dc.subjectText miningen
dc.subjectWeb miningen
dc.subjectManagementen
dc.subjectCybercriminalen
dc.titleSecuring Cyberspace: Analyzing Cybercriminal Communities through Web and Text Mining Perspectivesen_US
dc.typetexten
dc.typeElectronic Dissertationen
thesis.degree.grantorUniversity of Arizonaen
thesis.degree.leveldoctoralen
dc.contributor.committeememberNunamaker, Jayen
dc.contributor.committeememberValacich, Josephen
dc.contributor.committeememberChen, Hsinchunen
thesis.degree.disciplineGraduate Collegeen
thesis.degree.disciplineManagementen
thesis.degree.namePh.D.en
refterms.dateFOA2018-06-14T00:07:01Z
html.description.abstractCybersecurity has become one of the most pressing issues facing society today. In particular, cybercriminals often congregate within online communities to exchange knowledge and assets. As a result, there has been a strong interest in recent years in developing a deeper understanding on cybercriminal behaviors, the global cybercriminal supply chain, emerging threats, and various other cybersecurity-related activities. However, few works in recent years have focused on identifying, collecting, and analyzing cybercriminal contents. Despite the high societal impact of cybercriminal community research, only a few studies have leveraged these rich data sources in their totality, and those that do often resort to manual data collection and analysis techniques. In this dissertation, I address two broad research questions: 1) In what ways can I advance cybersecurity as a science by scrutinizing the contents of online cybercriminal communities? and 2) How can I make use of computational methodologies to identify, collect, and analyze cybercriminal communities in an automated and scalable manner? To these ends, the dissertation comprises four essays. The first essay introduces a set of computational methodologies and research guidelines for conducting cybercriminal community research. To this point, there has been no literature establishing a clear route for non-technical and non-security researchers to begin studying such communities. The second essay examines possible motives for prolonged participation by individuals within cybercriminal communities. The third essay develops new neural network language model (NNLM) capabilities and applies them to cybercriminal community data in order to understand hacker-specific language evolution and to identify emerging threats. The last essay focuses on developing a NNLM-based framework for identifying information dissemination among varying international cybercriminal populations by examining multilingual cybercriminal forums. These essays help further establish cybersecurity as a science.


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