首页|A strong physical unclonable function with machine learning immunity for Internet of Things application

A strong physical unclonable function with machine learning immunity for Internet of Things application

扫码查看
The physical unclonable functions(PUFs)are novel cryptographic primitives in modern hardware security systems.Compared with traditional alternatives based on digital keys and non-volatile memory(NVM),the PUF system shows great unclonability,high efficiency,and physical attack resilience.However,the conventional PUF design suffers from weak machine learning immunity,high storage overhead,and unreliability,making it difficult to implement in the Internet of Things(IoT)and edge computing applications.This paper presents a new PUF design that could solve the proposed obstacles.By utilizing the emission probability of traps commonly found in nano-scaled transistors,a model-based PUF system with strong machine learning resistance could be achieved.This PUF design,called Prob-PUF,needs fewer challenge-response pairs(CRPs)space and reveals superior resistance to modeling attacks due to the mixture of stable/random bits in its output response.Moreover,the Prob-PUF system could reach a high level of uniqueness and robustness,making it a potential candidate for future cryptographically secured protocols within the IoT.

physical unclonable function(PUF)electron trapsauthenticationencryptioncryptographysecurity

Pengpeng REN、Yongkang XUE、Linglin JING、Lining ZHANG、Runsheng WANG、Zhigang JI

展开 >

National Key Laboratory of Science and Technology on Micro/Nano Fabrication,Shanghai Jiao Tong University,Shanghai 200240,China

Department of Micro/Nano Electronics,Shanghai Jiao Tong University,Shanghai 200240,China

School of Electronic and Computer Engineering,Peking University,Shenzhen 518055,China

School of Integrated Circuits,Peking University,Beijing 100871,China

展开 >

National Key R&D Program of China

2019YFB2205005

2024

中国科学:信息科学(英文版)
中国科学院

中国科学:信息科学(英文版)

CSTPCDEI
影响因子:0.715
ISSN:1674-733X
年,卷(期):2024.67(1)
  • 1