0.3 pJ/Bit Machine Learning Resistant Strong PUF Using Subthreshold Voltage Divider Array

Abilash Venkatesh; Aishwarya Bahudhanam Venkatasubramaniyan; Xiaodan Xi; Arindam Sanyal

doi:10.1109/TCSII.2019.2943121

0.3 pJ/Bit Machine Learning Resistant Strong PUF Using Subthreshold Voltage Divider Array

Abilash Venkatesh, Aishwarya Bahudhanam Venkatasubramaniyan, Xiaodan Xi, Arindam Sanyal

Research output: Contribution to journal › Article › peer-review

21 Scopus citations

Abstract

This brief presents a subthreshold voltage divider based strong physical unclonable function (PUF). The PUF derives its uniqueness from random mismatch in threshold voltage in an inverter with gate and drain shorted and biased in subthreshold region. The nonlinear current-voltage relationship in subthreshold region also makes the proposed PUF resistant to machine learning (ML) based attacks. Prediction accuracy of PUF response with logistic regression, support vector machine (SVM) and multi-layer perceptron (MLP) is close to 51%. A prototype PUF fabricated in 65nm consumes only 0.3pJ/bit, and achieves the best combination of energy efficiency and resistance to ML attacks. The measured inter and intra hamming distance (HD) for the PUF are 0.5026 and 0.0466 respectively.

Original language	English (US)
Article number	8846235
Pages (from-to)	1394-1398
Number of pages	5
Journal	IEEE Transactions on Circuits and Systems II: Express Briefs
Volume	67
Issue number	8
DOIs	https://doi.org/10.1109/TCSII.2019.2943121
State	Published - Aug 2020
Externally published	Yes

Keywords

Physical unclonable function (PUF)
hardware security
machine learning
strong PUF

ASJC Scopus subject areas

Electrical and Electronic Engineering

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10.1109/TCSII.2019.2943121

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title = "0.3 pJ/Bit Machine Learning Resistant Strong PUF Using Subthreshold Voltage Divider Array",

abstract = "This brief presents a subthreshold voltage divider based strong physical unclonable function (PUF). The PUF derives its uniqueness from random mismatch in threshold voltage in an inverter with gate and drain shorted and biased in subthreshold region. The nonlinear current-voltage relationship in subthreshold region also makes the proposed PUF resistant to machine learning (ML) based attacks. Prediction accuracy of PUF response with logistic regression, support vector machine (SVM) and multi-layer perceptron (MLP) is close to 51%. A prototype PUF fabricated in 65nm consumes only 0.3pJ/bit, and achieves the best combination of energy efficiency and resistance to ML attacks. The measured inter and intra hamming distance (HD) for the PUF are 0.5026 and 0.0466 respectively.",

keywords = "Physical unclonable function (PUF), hardware security, machine learning, strong PUF",

author = "Abilash Venkatesh and Venkatasubramaniyan, {Aishwarya Bahudhanam} and Xiaodan Xi and Arindam Sanyal",

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AU - Venkatasubramaniyan, Aishwarya Bahudhanam

AU - Xi, Xiaodan

AU - Sanyal, Arindam

PY - 2020/8

Y1 - 2020/8

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AB - This brief presents a subthreshold voltage divider based strong physical unclonable function (PUF). The PUF derives its uniqueness from random mismatch in threshold voltage in an inverter with gate and drain shorted and biased in subthreshold region. The nonlinear current-voltage relationship in subthreshold region also makes the proposed PUF resistant to machine learning (ML) based attacks. Prediction accuracy of PUF response with logistic regression, support vector machine (SVM) and multi-layer perceptron (MLP) is close to 51%. A prototype PUF fabricated in 65nm consumes only 0.3pJ/bit, and achieves the best combination of energy efficiency and resistance to ML attacks. The measured inter and intra hamming distance (HD) for the PUF are 0.5026 and 0.0466 respectively.

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0.3 pJ/Bit Machine Learning Resistant Strong PUF Using Subthreshold Voltage Divider Array

Abstract

Keywords

ASJC Scopus subject areas

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