Machine Learning Attacks-Resistant Security by Mixed-Assembled Layers-Inserted Graphene Physically Unclonable Function
DC Field | Value | Language |
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dc.contributor.author | Lee, Subin | - |
dc.contributor.author | Jang, Byung Chul | - |
dc.contributor.author | Kim, Minseo | - |
dc.contributor.author | Lim, Si Heon | - |
dc.contributor.author | Ko, Eunbee | - |
dc.contributor.author | Kim, Hyun Ho | - |
dc.contributor.author | Yoo, Hocheon | - |
dc.date.accessioned | 2024-04-10T10:30:21Z | - |
dc.date.available | 2024-04-10T10:30:21Z | - |
dc.date.issued | 2023-10 | - |
dc.identifier.issn | 2198-3844 | - |
dc.identifier.issn | 2198-3844 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/90947 | - |
dc.description.abstract | Mixed layers of octadecyltrichlorosilane (ODTS) and 1H,1H,2H,2H-perfluorooctyltriethoxysilane (FOTS) on an active layer of graphene are used to induce a disordered doping state and form a robust defense system against machine-learning attacks (ML attacks). The resulting security key is formed from a 12 x 12 array of currents produced at a low voltage of 100 mV. The uniformity and inter-Hamming distance (HD) of the security key are 50.0 & PLUSMN; 12.3% and 45.5 & PLUSMN; 16.7%, respectively, indicating higher security performance than other graphene-based security keys. Raman spectroscopy confirmed the uniqueness of the 10,000 points, with the degree of shift of the G peak distinguishing the number of carriers. The resulting defense system has a 10.33% ML attack accuracy, while a FOTS-inserted graphene device is easily predictable with a 44.81% ML attack accuracy. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | WILEY | - |
dc.title | Machine Learning Attacks-Resistant Security by Mixed-Assembled Layers-Inserted Graphene Physically Unclonable Function | - |
dc.type | Article | - |
dc.identifier.wosid | 001049296200001 | - |
dc.identifier.doi | 10.1002/advs.202302604 | - |
dc.identifier.bibliographicCitation | ADVANCED SCIENCE, v.10, no.30 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85168083323 | - |
dc.citation.title | ADVANCED SCIENCE | - |
dc.citation.volume | 10 | - |
dc.citation.number | 30 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | graphene | - |
dc.subject.keywordAuthor | machine learning attack | - |
dc.subject.keywordAuthor | physical unclonable function | - |
dc.subject.keywordAuthor | raman spectroscopy | - |
dc.subject.keywordAuthor | self-assembled monolayer | - |
dc.subject.keywordPlus | FIELD-EFFECT TRANSISTORS | - |
dc.subject.keywordPlus | ELECTRONIC-STRUCTURE | - |
dc.subject.keywordPlus | CHARGE-TRANSFER | - |
dc.subject.keywordPlus | RAMAN | - |
dc.subject.keywordPlus | PERFORMANCE | - |
dc.subject.keywordPlus | STRAIN | - |
dc.subject.keywordPlus | PUF | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Nanoscience & Nanotechnology | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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