[Resource Topic] 2019/566: Deep Learning based Model Building Attacks on Arbiter PUF Compositions

Welcome to the resource topic for 2019/566

Deep Learning based Model Building Attacks on Arbiter PUF Compositions

Authors: Pranesh Santikellur, Aritra Bhattacharyay, Rajat Subhra Chakraborty


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.

ePrint: https://eprint.iacr.org/2019/566

See all topics related to this paper.

Feel free to post resources that are related to this paper below.

Example resources include: implementations, explanation materials, talks, slides, links to previous discussions on other websites.

For more information, see the rules for Resource Topics .