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dc.contributor.authorTian, Keen_US
dc.date.accessioned2018-09-14T08:00:26Z
dc.date.available2018-09-14T08:00:26Z
dc.date.issued2018-09-13
dc.identifier.othervt_gsexam:16471en_US
dc.identifier.urihttp://hdl.handle.net/10919/85013
dc.description.abstractThis thesis presents machine-learning based malware detection and post-detection rewriting techniques for mobile and web security problems. In mobile malware detection, we focus on detecting repackaged mobile malware. We design and demonstrate an Android repackaged malware detection technique based on code heterogeneity analysis. In post-detection rewriting, we aim at enhancing app security with bytecode rewriting. We describe how flow- and sink-based risk prioritization improves the rewriting scalability. We build an interface prototype with natural language processing, in order to customize apps according to natural language inputs. In web malware detection for Iframe injection, we present a tag-level detection system that aims to detect the injection of malicious Iframes for both online and offline cases. Our system detects malicious iframe by combining selective multi-execution and machine learning algorithms. We design multiple contextual features, considering Iframe style, destination and context properties.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.subjectmobile securityen_US
dc.subjectweb securityen_US
dc.subjectmachine learningen_US
dc.titleLearning-based Cyber Security Analysis and Binary Customization for Securityen_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.committeememberTan, Gangen_US
dc.contributor.committeememberRamakrishnan, Narendranen_US
dc.contributor.committeememberMeng, Naen_US
dc.contributor.committeememberRyder, Barbara Gershonen_US


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