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dc.contributor.authorMukhtar, Naila
dc.contributor.authorMehrabi, Mohamad
dc.contributor.authorKong, Yinan
dc.contributor.authorAnjum, Ashiq
dc.date.accessioned2019-06-13T13:45:10Z
dc.date.available2019-06-13T13:45:10Z
dc.date.issued2018-12-25
dc.identifier.citationMukhtar, N., Mehrabi, M., Kong, Y., and Anjum, A. (2019) 'Machine-learning-based side-channel evaluation of elliptic-curve cryptographic FPGA processor', Applied Sciences, 9(1), p.64. doi: 10.3390/app9010064.en_US
dc.identifier.issn2076-3417
dc.identifier.doi10.3390/app9010064
dc.identifier.urihttp://hdl.handle.net/10545/623850
dc.description.abstractSecurity of embedded systems is the need of the hour. A mathematically secure algorithm runs on a cryptographic chip on these systems, but secret private data can be at risk due to side-channel leakage information. This research focuses on retrieving secret-key information, by performing machine-learning-based analysis on leaked power-consumption signals, from Field Programmable Gate Array (FPGA) implementation of the elliptic-curve algorithm captured from a Kintex-7 FPGA chip while the elliptic-curve cryptography (ECC) algorithm is running on it. This paper formalizes the methodology for preparing an input dataset for further analysis using machine-learning-based techniques to classify the secret-key bits. Research results reveal how pre-processing filters improve the classification accuracy in certain cases, and show how various signal properties can provide accurate secret classification with a smaller feature dataset. The results further show the parameter tuning and the amount of time required for building the machine-learning modelsen_US
dc.description.sponsorshipUniversity of Derbyen_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.urlhttp://www.mdpi.com/2076-3417/9/1/64en_US
dc.subjectside-channel analysisen_US
dc.subjectpower analysis attacken_US
dc.subjectEmbedded Systemsen_US
dc.subjectMachine Learning Classificationen_US
dc.titleMachine-learning-based side-channel evaluation of elliptic-curve cryptographic FPGA processor.en_US
dc.typeArticleen_US
dc.contributor.departmentUniversity of Derbyen_US
dc.contributor.departmentMacquarie Universityen_US
dc.identifier.journalApplied Sciencesen_US
dc.source.journaltitleApplied Sciences
dc.source.volume9
dc.source.issue1
dc.source.beginpage64
dcterms.dateAccepted2018-12-18
refterms.dateFOA2019-06-13T13:45:11Z
dc.author.detail781847en_US


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