2 research outputs found
Deep Learning based Model Building Attacks on Arbiter PUF Compositions
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