PAC Learning of Arbiter PUFs

Fatemeh Ganji; Shahin Tajik; Jean-Pierre Seifert

Paper 2015/378

PAC Learning of Arbiter PUFs

Fatemeh Ganji, Shahin Tajik, and Jean-Pierre Seifert

Abstract

The general concept of Physically Unclonable Functions (PUFs) has been nowadays widely ac cepted 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.

Metadata

Available format(s): PDF
Publication info: Published elsewhere. Minor revision. Journal of Cryptographic Engineering
DOI: 10.1007/s13389-016-0119-4
Keywords: Arbiter PUF Deterministic Finite Automata Machine Learning PAC Learning Regular Language
Contact author(s): stajik @ sec t-labs tu-berlin de
History: 2016-02-13: revised; 2015-04-28: received; See all versions
Short URL: https://ia.cr/2015/378
License: CC BY

BibTeX

@misc{cryptoeprint:2015/378,
      author = {Fatemeh Ganji and Shahin Tajik and Jean-Pierre Seifert},
      title = {{PAC} Learning of Arbiter {PUFs}},
      howpublished = {Cryptology {ePrint} Archive, Paper 2015/378},
      year = {2015},
      doi = {10.1007/s13389-016-0119-4},
      url = {https://eprint.iacr.org/2015/378}
}