PAC learning of arbiter PUFs

The general concept of physically unclonable functions (PUFs) has been nowadays widely accepted and adopted to meet the requirements of secure identification and key generation/storage for cryptographic ciphers. However, shattered by different attacks, e.g., modeling attacks, it has been proved that the promised security features of arbiter PUFs, including unclonability and unpredictability, are not supported unconditionally. However, so far the success of existing modeling attacks relies on pure trial and error estimates. This means that neither the probability of obtaining a useful model (confidence), nor the sufficient number of CRPs, nor the probability of correct prediction (accuracy) is guaranteed. To address these issues, this work presents a probably approximately correct (PAC) learning algorithm. Based on a crucial discretization process, we are able to define a Deterministic finite automaton (of polynomial size), which exactly accepts the regular language corresponding to the challenges mapped by the given PUF to one responses.

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