A Computationally Efficient Tensor Regression Network based Modeling Attack on XOR Arbiter PUF and its Variants

@article{Santikellur2020Computationally,

title = {A Computationally Efficient Tensor Regression Network based Modeling Attack on XOR Arbiter PUF and its Variants},

author = {Pranesh Santikellur and Rajat Subhra Chakraborty},

doi = {10.1109/TCAD.2020.3032624},

issn = {1937-4151},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems},

pages = {1-1},

abstract = {XOR Arbiter PUF (XOR APUF), where the outputs of multiple APUFs are XOR-ed, has proven to be more robust to machine learning based modeling attacks. The reported successful modeling attacks for XOR APUF either employ auxiliary side-channel or reliability information, or require enormous computational effort. This robustness is primarily due to the difficulty in learning the unknown internal delay parameter terms in the mathematical model of a XOR APUF, and the robustness increases as the number of APUFs being XOR-ed increases. In this paper, we employ a novel machine learning based modeling technique called efficient CANDECOMP/PARAFAC-Tensor Regression Network (CP-TRN), a variant of CP-decomposition based tensor regression network, to reduce the computational resource requirement of model building attacks on XOR APUF. We theoretically prove the reduction in computational complexity, as well as give supporting experimental results. In addition, our proposed technique does not require any auxiliary information, and is robust to noisy training data. The proposed technique allowed us to successfully model 64-bit 8-XOR APUF and 128-bit 7-XOR APUF on a single desktop workstation, with high prediction accuracy. Further, we extend the proposed modeling attack technique to XOR APUF variants, e.g. Lightweight Secure PUF (LSPUF), which rely on input challenge transformation. The modeling accuracy results obtained by us for the LSPUF are comparable with those obtained by applying other state-of-the-art techniques, while requiring less training data.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}