Power Supply Analysis for Device Verification

dc.contributor.advisorPatel, Chintan
dc.contributor.authorShey, James
dc.contributor.departmentComputer Science and Electrical Engineering
dc.contributor.programEngineering, Electrical
dc.date.accessioned2021-09-01T13:55:27Z
dc.date.available2021-09-01T13:55:27Z
dc.date.issued2020-01-20
dc.description.abstractWith the global spanning of integrated circuit (IC) and electronic device supply chains, the ability of an untrusted entity to alter an IC or device while it is in the supply chain increases. There is also an increased ability to intercept and alter or introduce an altered version of a product with a global supply chain. This work seeks to verify devices through the use of power supply analysis on three focus areas: Solid-State Drives (SSDs) operation verification, IC identification, and Field Programmable Gate Array (FPGA) bitstream verification. The first focus area is using power supply analysis on an SSD to ensure that the device is operating properly. This work started with identifying trim operation signatures and then using machine learning to reach near 100 percent accuracy at identifying the operation. Follow on applications include read and write classification, operating system identification, and malware detection on SSDs. The second focus area is using current analysis to identify alterations in an IC even if the IC is functionally equivalent. This work is done through simulation and captures process variation, noise, and aging at multiple different temperatures all with greater than an F1 score of 0.90 given at least 2.5 percent of the circuit had been altered. Additionally, various machine learning techniques are applied to increase performance and lower the detection floor to than 2 percent of the circuit and in most cases 1 percent of the circuit. The application for this focus area is the ability to identify ICs to ensure that the desired IC is used in a final product and make it more difficult for piraters. The final focus area is using current analysis to verify a bitstream that is on an FPGA. Given a set of known bitstreams, the presented method results in a minimum F1 score of 0.8832 at determining if a bitstream is the desired version. This is accomplished by using both support vector machines and neural networks to determine changes in the FPGA fabric. Altered circuits ranged from 0.087 to 20.05 percent change in the underlying fabric while maintaining a functionally equivalent implementation. The application of this ensures that the correct bitstream is running on an FPGA. The three focus areas independently help to verify a device but can be combined to more completely verify a device. Monitoring its operations on the device level as well as the chip level, whether the chip is an Application-Specific Integrated Circuit (ASIC) or an FPGA, can make pirating cost-prohibitive.
dc.formatapplication:pdf
dc.genredissertations
dc.identifierdoi:10.13016/m2ndbz-zu2t
dc.identifier.other12262
dc.identifier.urihttp://hdl.handle.net/11603/22841
dc.languageen
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Theses and Dissertations Collection
dc.relation.ispartofUMBC Graduate School Collection
dc.relation.ispartofUMBC Student Collection
dc.sourceOriginal File Name: Shey_umbc_0434D_12262.pdf
dc.subjectFPGA
dc.subjectIC Identification
dc.subjectMachine Learning
dc.subjectSSD
dc.titlePower Supply Analysis for Device Verification
dc.typeText
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dcterms.accessRightsThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu

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