Memristor‐Based Security Primitives Robust to Malicious Attacks for Highly Secure Neuromorphic Systems
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 reporte...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-11-01
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| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202200177 |
| _version_ | 1798015664021045248 |
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| author | Jungyeop Oh Sungkyu Kim Junhwan Choi Jun-Hwe Cha Sung Gap Im Byung Chul Jang Sung-Yool Choi |
| author_facet | Jungyeop Oh Sungkyu Kim Junhwan Choi Jun-Hwe Cha Sung Gap Im Byung Chul Jang Sung-Yool Choi |
| author_sort | Jungyeop Oh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T15:37:28Z |
| format | Article |
| id | doaj.art-db4f841c0da140fa8b20193c16ade7fb |
| institution | Directory Open Access Journal |
| issn | 2640-4567 |
| language | English |
| last_indexed | 2024-04-11T15:37:28Z |
| publishDate | 2022-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj.art-db4f841c0da140fa8b20193c16ade7fb2022-12-22T04:15:55ZengWileyAdvanced Intelligent Systems2640-45672022-11-01411n/an/a10.1002/aisy.202200177Memristor‐Based Security Primitives Robust to Malicious Attacks for Highly Secure Neuromorphic SystemsJungyeop Oh0Sungkyu Kim1Junhwan Choi2Jun-Hwe Cha3Sung Gap Im4Byung Chul Jang5Sung-Yool Choi6School of Electrical Engineering Graphene/2D Materials Research Center Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of KoreaDepartment of Nanotechnology and Advanced Materials Engineering Sejong University 209 Neungdong-ro, Gwangjin-gu Seoul 05006 Republic of KoreaDepartment of Chemical and Biomolecular Engineering Graphene/2D Materials Research Center Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of KoreaSchool of Electrical Engineering Graphene/2D Materials Research Center Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of KoreaDepartment of Chemical and Biomolecular Engineering Graphene/2D Materials Research Center Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of KoreaSchool of Electronics Engineering Kyungpook National University 80 Daehakro, Bukgu Daegu 41566 Republic of KoreaSchool of Electrical Engineering Graphene/2D Materials Research Center Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of KoreaInternet‐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.https://doi.org/10.1002/aisy.202200177cryptographymachine learning attacksmemristorsneuromorphic systemsphysical unclonable functions |
| spellingShingle | Jungyeop Oh Sungkyu Kim Junhwan Choi Jun-Hwe Cha Sung Gap Im Byung Chul Jang Sung-Yool Choi Memristor‐Based Security Primitives Robust to Malicious Attacks for Highly Secure Neuromorphic Systems Advanced Intelligent Systems cryptography machine learning attacks memristors neuromorphic systems physical unclonable functions |
| title | Memristor‐Based Security Primitives Robust to Malicious Attacks for Highly Secure Neuromorphic Systems |
| title_full | Memristor‐Based Security Primitives Robust to Malicious Attacks for Highly Secure Neuromorphic Systems |
| title_fullStr | Memristor‐Based Security Primitives Robust to Malicious Attacks for Highly Secure Neuromorphic Systems |
| title_full_unstemmed | Memristor‐Based Security Primitives Robust to Malicious Attacks for Highly Secure Neuromorphic Systems |
| title_short | Memristor‐Based Security Primitives Robust to Malicious Attacks for Highly Secure Neuromorphic Systems |
| title_sort | memristor based security primitives robust to malicious attacks for highly secure neuromorphic systems |
| topic | cryptography machine learning attacks memristors neuromorphic systems physical unclonable functions |
| url | https://doi.org/10.1002/aisy.202200177 |
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