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dc.contributor.advisorYang, Wei
dc.creatorChauhan, Anki
dc.date.accessioned2020-08-24T15:01:09Z
dc.date.available2020-08-24T15:01:09Z
dc.date.created2020-05
dc.date.issued2020-05
dc.date.submittedMay 2020
dc.identifier.urihttps://hdl.handle.net/10735.1/8810
dc.description.abstractThis thesis concerns the study of Machine Learning based methods for detecting vulnerable code. Various Neural Network models have been trained to detect specific vulnerabilities on a programming language dataset. This work, entails an approach not targeting specific vulnerabilities. We also leverage the commonality among programming languages like JAVA and C# by training the model on both languages and detecting vulnerabilities.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.rights©2020 Anki Chauhan. All rights reserved.
dc.subjectMachine learning
dc.subjectComputer security--Software
dc.subjectSource code (Computer science)
dc.subjectComputer networks--Security measures--Software
dc.titleMachine Learning Based Cross-Language Vulnerability Detection : How Far Are We
dc.typeThesis
dc.date.updated2020-08-24T15:01:10Z
dc.type.materialtext
dc.contributor.ORCID0000-0003-2812-1907 (Chauhan, A)
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.departmentComputer Science
thesis.degree.levelMasters
thesis.degree.nameMS


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