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dc.contributor.authorLiu, Fangen_US
dc.date.accessioned2017-08-10T08:00:46Z
dc.date.available2017-08-10T08:00:46Z
dc.date.issued2017-08-09en_US
dc.identifier.othervt_gsexam:12266en_US
dc.identifier.urihttp://hdl.handle.net/10919/78684
dc.description.abstractCyber security risk has been a problem ever since the appearance of telecommunication and electronic computers. In the recent 30 years, researchers have developed various tools to protect the confidentiality, integrity, and availability of data and programs. However, new challenges are emerging as the amount of data grows rapidly in the big data era. On one hand, attacks are becoming stealthier by concealing their behaviors in massive datasets. One the other hand, it is becoming more and more difficult for existing tools to handle massive datasets with various data types. This thesis presents the attempts to address the challenges and solve different security problems by mining security risks from massive datasets. The attempts are in three aspects: detecting security risks in the enterprise environment, prioritizing security risks of mobile apps and measuring the impact of security risks between websites and mobile apps. First, the thesis presents a framework to detect data leakage in very large content. The framework can be deployed on cloud for enterprise and preserve the privacy of sensitive data. Second, the thesis prioritizes the inter-app communication risks in large-scale Android apps by designing new distributed inter-app communication linking algorithm and performing nearest-neighbor risk analysis. Third, the thesis measures the impact of deep link hijacking risk, which is one type of inter-app communication risks, on 1 million websites and 160 thousand mobile apps. The measurement reveals the failure of Google's attempts to improve the security of deep links.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectCyber Securityen_US
dc.subjectBig Data Securityen_US
dc.subjectMobile Securityen_US
dc.subjectData Leakage Detectionen_US
dc.titleMining Security Risks from Massive Datasetsen_US
dc.typeDissertationen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Science and Applicationsen_US
dc.contributor.committeechairYao, Danfengen_US
dc.contributor.committeememberXu, Dongyanen_US
dc.contributor.committeememberPrakash, Bodicherla Adityaen_US
dc.contributor.committeememberButt, Alien_US
dc.contributor.committeememberLou, Wenjingen_US


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