Hardware-Based Workload Forensics and Malware Detection in Modern Microprocessors

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dc.contributor.advisorMakris, Yiorgos
dc.creatorZhou, Liwei
dc.date.accessioned2020-04-01T12:28:40Z
dc.date.available2020-04-01T12:28:40Z
dc.date.created2018-12
dc.date.issued2018-12
dc.date.submittedDecember 2018
dc.date.updated2020-04-01T12:28:41Z
dc.description.abstractTraditional computer forensics and/or malware detection methods are generally implemented at the operating system (OS) or the hypervisor level, which benefits from abundant software semantics and implementation flexibility. Nevertheless, the data logging and monitoring systems involved in these methods are vulnerable to spoofing attacks at the same level, which undermine their effectiveness. In this dissertation, the hardware-based methodologies are proposed to perform workload forensics and/or malware detection in microprocessors. In contrast to the software-based counterparts, a hardware-based implementation ensures the immunity to software tampering. Specifically, a generic architecture is introduced which a hardware-based forensic analysis or a malware detection method needs to follow, as well as the various architecture-level information which could potentially be harnessed to ensure system security and/or integrity. To illustrate the proposed concept, two incarnations, i.e., hardware-based workload forensics and hardware-based rootkit detection are present. Experimental results corroborate that even a low-cost hardware implementation can facilitate highly successful forensics analysis and/or malware detection, while taking advantage of its innate immunity to software-based attacks.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10735.1/7737
dc.language.isoen
dc.rights©2018 Liwei Zhou. All Rights Reserved.
dc.subjectDigital forensic science
dc.subjectMalware (Computer software)
dc.subjectComputer security
dc.subjectComputer input-output equipment
dc.subjectMachine learning
dc.subjectMicroprocessors
dc.titleHardware-Based Workload Forensics and Malware Detection in Modern Microprocessors
dc.typeDissertation
dc.type.materialtext
thesis.degree.departmentElectrical Engineering
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.levelDoctoral
thesis.degree.namePHD

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