LBIST-PUF: An LBIST Scheme Towards Efficient Challenge-Response Pairs Collection and Machine-Learning Attack Tolerance Improvement

Abstract

Device identification using challenge-response pairs (CRPs), in which the response is obtained from a physically unclonable function (PUF), is a promising countermeasure for the counterfeit of integrated circuits (ICs). To achieve secure device identification, a large number of CRPs are collected by the manufacturers, thereby increasing the measurement costs. This paper proposes a novel scheme, which employs a logic built-in self-test (LBIST) circuit, to efficiently collect the CRPs during production tests. As a result, no additional measurement is required for the CRP collection. In addition, the proposed technique can counter machine-learning (ML) attacks because of the complicated relationship between challenge and response through the LBIST circuit. Through the proof-of-concept implementation, in which a field-programmable gate array (FPGA) is used, we demonstrate the PUF performance can be evaluated by a test pattern generated by the LBIST circuit. Furthermore, the vulnerability due to ML attacks using a support vector machine (SVM) and random forest (RF) is lowered by more than two times compared to the naive usage of PUF.