Applied Artificial Neural Network for Nuclear Cyber Forensics Using a Field Programmable Gate Array

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dc.contributor.advisorAlamaniotis, Miltos
dc.contributor.authorKelps, Bryan
dc.contributor.committeeMemberAhmed, Sara
dc.contributor.committeeMemberJohn, Eugene
dc.date.accessioned2024-03-26T22:49:47Z
dc.date.available2024-03-26T22:49:47Z
dc.date.issued2023
dc.description.abstractCritical infrastructure for cities and countries are becoming increasingly vulnerable. There has been a history of various attacks on energy generation to include Nuclear Power Plants(NPP). To defend against cyber-attacks against NPPs, many solutions have been proposed. Some of those include using traditional software security measure, software-based neural networks, and FPGAs to filter malicious and benign network traffic using whitelists. This thesis proposes using an Artificial Neural Network on an FPGA to determine the maliciousness of network traffic. This will have the advantage of speed due to hardware advantages, and ANNs are quick to compute once trained. The Arty A7-100T was the development board selected for implementation, and the board hosts the XC7A100TICSG324-1 FPGA with MicroBlaze processor capabilities. The system was able to successful simulate an accurate prediction of connections within 1.5 microseconds.
dc.description.departmentElectrical and Computer Engineering
dc.format.extent1 electronic resource (81 pages)
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9798381181715
dc.identifier.urihttps://hdl.handle.net/20.500.12588/6287
dc.languageeng
dc.subjectArtificial Neural Network
dc.subjectCORDIC
dc.subjectCyber Forensics
dc.subjectFPGA
dc.subjectNuclear
dc.subject.classificationElectrical engineering
dc.subject.classificationArtificial intelligence
dc.subject.classificationComputer engineering
dc.titleApplied Artificial Neural Network for Nuclear Cyber Forensics Using a Field Programmable Gate Array
dc.typeThesis
dc.type.dcmiText
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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