DC Field | Value | Language |
---|---|---|
dc.contributor.author | Oh, Jungyeop | ko |
dc.contributor.author | Kim, Sungkyu | ko |
dc.contributor.author | Choi, Junhwan | ko |
dc.contributor.author | Cha, Jun-Hwe | ko |
dc.contributor.author | Im, Sung Gap | ko |
dc.contributor.author | Jang, Byung Chul | ko |
dc.contributor.author | Choi, Sung-Yool | ko |
dc.date.accessioned | 2022-11-28T06:01:15Z | - |
dc.date.available | 2022-11-28T06:01:15Z | - |
dc.date.created | 2022-10-17 | - |
dc.date.created | 2022-10-17 | - |
dc.date.created | 2022-10-17 | - |
dc.date.created | 2022-10-17 | - |
dc.date.issued | 2022-11 | - |
dc.identifier.citation | ADVANCED INTELLIGENT SYSTEMS, v.4, no.11 | - |
dc.identifier.issn | 2640-4567 | - |
dc.identifier.uri | http://hdl.handle.net/10203/301112 | - |
dc.description.abstract | Internet-of-things (IoT) edge devices with a memristive neuromorphic system can more effectively enhance daily lives. However, cyberattacks remain critical concerns for smart IoT edge devices that process a vast body of information via networks. Herein, a highly secure neuromorphic system is reported, which can be implemented using a physically unclonable function (PUF) that exploits the high entropy achieved via the stochastic switching of a poly(1,3,5-trivinyl-1,3,5-trimethyl cyclotrisiloxane) (pV3D3)-based memristor. The excellent insulating property of pV3D3 enhances the stochasticity of the tunneling distance for randomly ruptured Cu filaments. The pV3D3 memristor-based PUF (pV3D3-PUF) achieves near-ideal 50% averages for uniformity and uniqueness, excellent reliability under conditions of mechanical stress and water immersion, and reconfigurability-bolstering security without additional hardware. Using stochastic in-memory computing, the pV3D3-PUF shows resilience to machine learning attacks. Furthermore, a cryptography protocol is demonstrated, which enables artificial intelligence service implementation without security issues for PUF-integrated pV3D3 memristor-based neuromorphic systems. | - |
dc.language | English | - |
dc.publisher | WILEY | - |
dc.title | Memristor-Based Security Primitives Robust to Malicious Attacks for Highly Secure Neuromorphic Systems | - |
dc.type | Article | - |
dc.identifier.wosid | 000863497600001 | - |
dc.type.rims | ART | - |
dc.citation.volume | 4 | - |
dc.citation.issue | 11 | - |
dc.citation.publicationname | ADVANCED INTELLIGENT SYSTEMS | - |
dc.identifier.doi | 10.1002/aisy.202200177 | - |
dc.contributor.localauthor | Im, Sung Gap | - |
dc.contributor.localauthor | Choi, Sung-Yool | - |
dc.contributor.nonIdAuthor | Kim, Sungkyu | - |
dc.contributor.nonIdAuthor | Jang, Byung Chul | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | cryptography | - |
dc.subject.keywordAuthor | machine learning attacks | - |
dc.subject.keywordAuthor | memristors | - |
dc.subject.keywordAuthor | neuromorphic systems | - |
dc.subject.keywordAuthor | physical unclonable functions | - |
dc.subject.keywordPlus | MEMORY ARRAY | - |
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