Machine-Learning Attacks on PolyPUFs, OB-PUFs, RPUFs, LHS-PUFs, and PUF–FSMs

A physically unclonable function (PUF) is a circuit of which the input-output behavior is designed to be sensitive to the random variations of its manufacturing process. This building block hence facilitates the authentication of any given device in a population of identically laid-out silicon chips, similar to the biometric authentication of a human. The focus and novelty of this paper is the development of efficient impersonation attacks on the following five Arbiter PUF-based authentication protocols: 1) the so-called Poly PUF protocol of Konigsmark et al. as published in the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems in 2016; 2) the so-called OB-PUF protocol of Gao et al. as presented at the IEEE Conference PerCom 2016; 3) the so-called RPUF protocol of Ye et al. as presented at the IEEE Conference AsianHOST 2016; 4) the so-called LHS-PUF protocol of Idriss and Bayoumi as presented at the IEEE Conference RFID-TA 2017; and 5) the so-called PUF-FSM protocol of Gao et al. as published in the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems in 2018. The common flaw of all five designs is that the use of lightweight obfuscation logic provides insufficient protection against machine-learning attacks.