A Machine Learning Attacks Resistant Two Stage Physical Unclonable Functions Design
| Title | A Machine Learning Attacks Resistant Two Stage Physical Unclonable Functions Design |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Su, H., Zwolinski, M., Halak, B. |
| Conference Name | 2018 IEEE 3rd International Verification and Security Workshop (IVSW) |
| ISBN Number | 978-1-5386-6544-2 |
| Keywords | 32-bit current mirror, 65nm CMOS technology, Arbiter-PUF, asynchronous circuits, CMOS integrated circuits, composability, cryptography, Current Mirror PUF, enhancing security, Hash functions, learning (artificial intelligence), lightweight electronics, machine learning, machine learning attacks, Mathematical model, mirrors, modelling attacks, physical unclonable function (PUF), privacy, pubcrawl, PUF, reliability, resilience, Resiliency, security, security applications, security of data, stage physical unclonable functions design, traditional approaches, Transistors |
| Abstract | Physical Unclonable Functions (PUFs) have been designed for many security applications such as identification, authentication of devices and key generation, especially for lightweight electronics. Traditional approaches to enhancing security, such as hash functions, may be expensive and resource dependent. However, modelling attacks using machine learning (ML) show the vulnerability of most PUFs. In this paper, a combination of a 32-bit current mirror and 16-bit arbiter PUFs in 65nm CMOS technology is proposed to improve resilience against modelling attacks. Both PUFs are vulnerable to machine learning attacks and we reduce the output prediction rate from 99.2% and 98.8% individually, to 60%. |
| URL | https://ieeexplore.ieee.org/document/8494839 |
| DOI | 10.1109/IVSW.2018.8494839 |
| Citation Key | su_machine_2018 |
- mirrors
- Transistors
- traditional approaches
- stage physical unclonable functions design
- security of data
- security applications
- security
- Resiliency
- resilience
- Reliability
- PUF
- pubcrawl
- privacy
- physical unclonable function (PUF)
- modelling attacks
- 32-bit current mirror
- Mathematical model
- machine learning attacks
- machine learning
- lightweight electronics
- learning (artificial intelligence)
- Hash functions
- enhancing security
- Current Mirror PUF
- Cryptography
- composability
- CMOS integrated circuits
- asynchronous circuits
- Arbiter-PUF
- 65nm CMOS technology