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dc.contributor.advisorLee, Wenke
dc.contributor.authorAlzahrani, Ibtehaj M.
dc.date.accessioned2020-05-20T16:57:50Z
dc.date.available2020-05-20T16:57:50Z
dc.date.created2019-05
dc.date.issued2019-04-26
dc.date.submittedMay 2019
dc.identifier.urihttp://hdl.handle.net/1853/62701
dc.description.abstractWe propose a tool to identify crashes caused by filed exploits from benign crashes, and cluster them based on the exploited vulnerabilities to prioritize crashes from a security point of view. The tool extracts features from crash reports and decides whether a crash caused by malicious behavior or not. In the case of malicious behavior, it identifies the attack type that generates the crash report; we are focusing on four attack types which are Heap exploitation, Shellcode injection, Format String attack, and Return Oriented Programming. Further, it clusters the crash reports based on the exploited vulnerabilities.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectAttack-driven crash reports
dc.titleIdentifying and clustering attack-driven crash reports using machine learning
dc.typeThesis
dc.description.degreeM.S.
dc.contributor.departmentComputer Science
thesis.degree.levelMasters
dc.contributor.committeeMemberAhamad, Mustaque
dc.contributor.committeeMemberKim, Taesoo
dc.date.updated2020-05-20T16:57:51Z


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