Memristor‐Based Security Primitives Robust to Malicious Attacks for Highly Secure Neuromorphic Systems. (2nd October 2022)
- Record Type:
- Journal Article
- Title:
- Memristor‐Based Security Primitives Robust to Malicious Attacks for Highly Secure Neuromorphic Systems. (2nd October 2022)
- Main Title:
- Memristor‐Based Security Primitives Robust to Malicious Attacks for Highly Secure Neuromorphic Systems
- Authors:
- Oh, Jungyeop
Kim, Sungkyu
Choi, Junhwan
Cha, Jun-Hwe
Im, Sung Gap
Jang, Byung Chul
Choi, Sung-Yool - Abstract:
- 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. Abstract : The highly secure neuromorphic system for smart IoT devices can be implemented using humidity‐resistant and flexible physically unclonable function (PUF) based on the initiated chemical vapor deposition polymer memristor array. The unique features of pV3D3Abstract : 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. Abstract : The highly secure neuromorphic system for smart IoT devices can be implemented using humidity‐resistant and flexible physically unclonable function (PUF) based on the initiated chemical vapor deposition polymer memristor array. The unique features of pV3D3 enable pV3D3‐PUF to achieve the key functionalities of security primitives. Furthermore, a cryptography protocol for the PUF‐integrated neuromorphic system is proposed. … (more)
- Is Part Of:
- Advanced intelligent systems. Volume 4:Number 11(2022)
- Journal:
- Advanced intelligent systems
- Issue:
- Volume 4:Number 11(2022)
- Issue Display:
- Volume 4, Issue 11 (2022)
- Year:
- 2022
- Volume:
- 4
- Issue:
- 11
- Issue Sort Value:
- 2022-0004-0011-0000
- Page Start:
- n/a
- Page End:
- n/a
- Publication Date:
- 2022-10-02
- Subjects:
- cryptography -- machine learning attacks -- memristors -- neuromorphic systems -- physical unclonable functions
Artificial intelligence -- Periodicals
Robotics -- Periodicals
Control theory -- Periodicals
006.3 - Journal URLs:
- http://onlinelibrary.wiley.com/ ↗
https://onlinelibrary.wiley.com/journal/26404567 ↗ - DOI:
- 10.1002/aisy.202200177 ↗
- Languages:
- English
- ISSNs:
- 2640-4567
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 24809.xml