Security Evaluation of RG-DTM PUF Using Machine Learning Attacks

Mitsuru SHIOZAKI  Kousuke OGAWA  Kota FURUHASHI  Takahiko MURAYAMA  Masaya YOSHIKAWA  Takeshi FUJINO  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E97-A   No.1   pp.275-283
Publication Date: 2014/01/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E97.A.275
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Cryptography and Information Security)
Category: Hardware Based Security
Keyword: 
physical unclonable function (PUF),  arbiter-PUF,  XOR arbiter-PUF,  RG-DTM PUF,  machine learning attack,  support vector machine (SVM),  logistic regression (LR),  

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Summary: 
In modern hardware security applications, silicon physical unclonable functions (PUFs) are of interest for their potential use as a unique identity or secret key that is generated from inherent characteristics caused by process variations. However, arbiter-based PUFs utilizing the relative delay-time difference between equivalent paths have a security issue in which the generated challenge-response pairs (CRPs) can be predicted by a machine learning attack. We previously proposed the RG-DTM PUF, in which a response is decided from divided time domains allocated to response 0 or 1, to improve the uniqueness of the conventional arbiter-PUF in a small circuit. However, its resistance against machine learning attacks has not yet been studied. In this paper, we evaluate the resistance against machine learning attacks by using a support vector machine (SVM) and logistic regression (LR) in both simulations and measurements and compare the RG-DTM PUF with the conventional arbiter-PUF and with the XOR arbiter-PUF, which strengthens the resistance by using XORing output from multiple arbiter-PUFs. In numerical simulations, prediction rates using both SVM and LR were above 90% within 1,000 training CRPs on the arbiter-PUF. The machine learning attack using the SVM could never predict responses on the XOR arbiter-PUF with over six arbiter-PUFs, whereas the prediction rate eventually reached 95% using the LR and many training CRPs. On the RG-DTM PUF, when the division number of the time domains was over eight, the prediction rates using the SVM were equal to the probability by guess. The machine learning attack using LR has the potential to predict responses, although an adversary would need to steal a significant amount of CRPs. However, the resistance can exponentially be strengthened with an increase in the division number, just like with the XOR arbiter-PUF. Over one million CRPs are required to attack the 16-divided RG-DTM PUF. Differences between the RG-DTM PUF and the XOR arbiter-PUF relate to the area penalty and the power penalty. Specifically, the XOR arbiter-PUF has to make up for resistance against machine learning attacks by increasing the circuit area, while the RG-DTM PUF is resistant against machine learning attacks with less area penalty and power penalty since only capacitors are added to the conventional arbiter-PUF. We also attacked RG-DTM PUF chips, which were fabricated with 0.18-µm CMOS technology, to evaluate the effect of physical variations and unstable responses. The resistance against machine learning attacks was related to the delay-time difference distribution, but unstable responses had little influence on the attack results.