Paper 2019/566

Deep Learning based Model Building Attacks on Arbiter PUF Compositions

Pranesh Santikellur, Aritra Bhattacharyay, and Rajat Subhra Chakraborty

Abstract

Robustness to modeling attacks is an important requirement for PUF circuits. Several reported Arbiter PUF com- positions have resisted modeling attacks. and often require huge computational resources for successful modeling. In this paper we present deep feedforward neural network based modeling attack on 64-bit and 128-bit Arbiter PUF (APUF), and several other PUFs composed of Arbiter PUFs, namely, XOR APUF, Lightweight Secure PUF (LSPUF), Multiplexer PUF (MPUF) and its variants (cMPUF and rMPUF), and the recently proposed Interpose PUF (IPUF, up to the (4,4)-IPUF configuration). The technique requires no auxiliary information (e.g. side-channel information or reliability information), while employing deep neural networks of relatively low structural complexity to achieve very high modeling accuracy at low computational overhead (compared to previously proposed approaches), and is reasonably robust to error-inflicted training dataset.

Metadata
Available format(s)
PDF
Category
Applications
Publication info
Preprint. Minor revision.
Keywords
physically unclonable functionmachine learningdeep learningarbiter pufPUF
Contact author(s)
pranesh sklr @ iitkgp ac in
rschakraborty @ cse iitkgp ac in
History
2019-09-23: revised
2019-05-27: received
See all versions
Short URL
https://ia.cr/2019/566
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2019/566,
      author = {Pranesh Santikellur and Aritra  Bhattacharyay and Rajat Subhra Chakraborty},
      title = {Deep Learning based Model Building Attacks on Arbiter PUF Compositions},
      howpublished = {Cryptology ePrint Archive, Paper 2019/566},
      year = {2019},
      note = {\url{https://eprint.iacr.org/2019/566}},
      url = {https://eprint.iacr.org/2019/566}
}
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