research-article

PUF designed with Resistive RAM and Ternary States

Authors:

Bertrand Cambou,

Marius OrlowskiAuthors Info & Claims

CISRC '16: Proceedings of the 11th Annual Cyber and Information Security Research Conference

Article No.: 1, Pages 1 - 8

https://doi.org/10.1145/2897795.2897808

Published: 05 April 2016 Publication History

Abstract

The designs of Physically Unclonable Functions (PUFs) described in this paper are based on Resistive RAMs incorporating ternary states with the objective to reduce false negative authentications (FNA) with low Challenge-Response-Pair (CRP) error rates. Unlike other error correction method, the method is not increasing false positive authentications (FPA). The ternary states, the "Xs", allow the blanking of all cells that are not characterized as consistently capable to generate stable and easy to read "1s" or "0s" PUF challenges. Experimental data extracted from Cu/TaOx/Pt Resistive RAM samples confirms that such a method can generate CRPs having error rates below 8 ppm useable for secure hardware authentication. Random Number Generators (RNG) can also be enhanced by the same ternary architecture.

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Cited By

Hadke SKang MSangwan VHersam M(2025)Two-Dimensional Materials for Brain-Inspired Computing HardwareChemical Reviews10.1021/acs.chemrev.4c00631Online publication date: 2-Jan-2025
https://doi.org/10.1021/acs.chemrev.4c00631
Khan REldeeb HMefgouda BAlhussein OSaleh HMuhaidat S(2025)Encoder decoder-based Virtual Physically Unclonable Function for Internet of Things device authentication using split-learningComputers and Security10.1016/j.cose.2024.104164148:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.cose.2024.104164
Ibrahim OSciancalepore SDi Pietro R(2024)MAG-PUFs: Authenticating IoT devices via electromagnetic physical unclonable functions and deep learningComputers & Security10.1016/j.cose.2024.103905143(103905)Online publication date: Aug-2024
https://doi.org/10.1016/j.cose.2024.103905
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cover image ACM Other conferences

CISRC '16: Proceedings of the 11th Annual Cyber and Information Security Research Conference

April 2016

150 pages

ISBN:9781450337526

DOI:10.1145/2897795

General Chairs:
Joseph P. Trien
Oak Ridge National Laboratory
,
Stacy J. Prowell
Oak Ridge National Laboratory
,
John R. Goodall
Oak Ridge National Laboratory
,
Program Chair:
Robert A. Bridges
Oak Ridge National Laboratory

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 05 April 2016

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CISRC '16

CISRC '16: 11th Annual Cyber and Information Security Research

April 5 - 7, 2016

TN, Oak Ridge, USA

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CISRC '16 Paper Acceptance Rate 11 of 28 submissions, 39%;

Overall Acceptance Rate 69 of 136 submissions, 51%

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Cited By

Hadke SKang MSangwan VHersam M(2025)Two-Dimensional Materials for Brain-Inspired Computing HardwareChemical Reviews10.1021/acs.chemrev.4c00631Online publication date: 2-Jan-2025
https://doi.org/10.1021/acs.chemrev.4c00631
Khan REldeeb HMefgouda BAlhussein OSaleh HMuhaidat S(2025)Encoder decoder-based Virtual Physically Unclonable Function for Internet of Things device authentication using split-learningComputers and Security10.1016/j.cose.2024.104164148:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.cose.2024.104164
Ibrahim OSciancalepore SDi Pietro R(2024)MAG-PUFs: Authenticating IoT devices via electromagnetic physical unclonable functions and deep learningComputers & Security10.1016/j.cose.2024.103905143(103905)Online publication date: Aug-2024
https://doi.org/10.1016/j.cose.2024.103905
Naveenkumar RSivamangai N(2024)Hardware Trojans Detection and Prevention Techniques ReviewWireless Personal Communications10.1007/s11277-024-11334-6136:2(1147-1182)Online publication date: 25-Jun-2024
https://doi.org/10.1007/s11277-024-11334-6
Jain SKorenda ACambou B(2024)A Novel Approach to Optimize Response-Based Cryptography for Secure Data TransmissionProceedings of the Future Technologies Conference (FTC) 2024, Volume 410.1007/978-3-031-73128-0_15(226-248)Online publication date: 6-Nov-2024
https://doi.org/10.1007/978-3-031-73128-0_15
Rios MPartridge MJain SCambou B(2024)True Random Numbers from Pairs of SRAM DevicesIntelligent Computing10.1007/978-3-031-62269-4_41(630-650)Online publication date: 21-Jun-2024
https://doi.org/10.1007/978-3-031-62269-4_41
Aguilar Rios MAlam MCambou B(2023)MRAM Devices to Design Ternary Addressable Physically Unclonable FunctionsElectronics10.3390/electronics1215330812:15(3308)Online publication date: 2-Aug-2023
https://doi.org/10.3390/electronics12153308
Rios MJain SCambou B(2023)Design of a True Random Number Generator Based on MRAM DevicesIntelligent Computing10.1007/978-3-031-37963-5_90(1320-1341)Online publication date: 20-Aug-2023
https://doi.org/10.1007/978-3-031-37963-5_90
Ibrahim OSciancalepore SDi Pietro R(2023)MAG-PUF: Magnetic Physical Unclonable Functions for Device Authentication in the IoTSecurity and Privacy in Communication Networks10.1007/978-3-031-25538-0_8(130-149)Online publication date: 4-Feb-2023
https://doi.org/10.1007/978-3-031-25538-0_8
Ali-Pour AHely DBeroulle VDi Natale G(2022)Strong PUF Enrollment with Machine Learning: A Methodical ApproachElectronics10.3390/electronics1104065311:4(653)Online publication date: 19-Feb-2022
https://doi.org/10.3390/electronics11040653
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