Cost‐Efficient Design for Modeling Attacks Resistant PUFs
Mohd Syafiq Mispan1,2,a, Haibo Su2,b, Mark Zwolinski2,c and Basel Halak2,d
1Electronics and Computer Science, University of Southampton, United Kingdom
amsm1g14@ecs.soton.ac.uk
2Faculty of Engineering Technology, Technical University of Malaysia Malacca, Malaysia
bH.Su@ecs.soton.ac.uk
cmz@ecs.soton.ac.uk
dbh9@ecs.soton.ac.uk
ABSTRACT
Physical Unclonable Functions (PUFs) exploit the intrinsic manufacturing process variations to generate a unique signature for each silicon chip; this technology allows building lightweight cryptographic primitive suitable for resourceconstrained devices. However, the vast majority of existing PUF design is susceptible to modeling attacks using machine learning technique, this means it is possible for an adversary to build a mathematical clone of the PUF that have the same challenge/response behavior of the device. Existing approaches to solve this problem include the use of hash functions, which can be prohibitively expensive and render PUF technology as the suitable candidate for lightweight security. This work presents a challenge permutation and substitution techniques which are both area and energy efficient. We implemented two examples of the proposed solution in 65‐nm CMOS technology, the first using a delay‐based structure design (an Arbiter‐PUF), and the second using subthreshold current design (two‐choose‐one PUF or TCO‐PUF). The resiliency of both architectures against modeling attacks is tested using an artificial neural network machine learning algorithm. The experiment results show that it is possible to reduce the predictability of PUFs to less than 70% and a fractional area and power costs compared to existing hash function approaches.
Keywords: Physical Unclonable Function (PUF), Arbiter-PUF, TCO‐PUF, Machine Learning, Security.
