Kernel-based Response Extraction Approach for Efficient Configurable Ring Oscillator PUF

Author:

ABULIBDEH ENAS1,Saleh Hani1,Mohammad Baker1,Qutayri Mahmoud Al1,Hussain Asif2

Affiliation:

1. Khalifa University of Science and Technology

2. Intel Technology

Abstract

Abstract

Physically Unclonable Function (PUF) is an emerging hardware security primitive that provides a promising solution for lightweight security. PUFs can be used to generate a secret key that depends on the manufacturing process variation for authentication and device identification. However, the resource requirements of PUFs pose challenges for their application in resource-constrained Internet of Things (IoT) devices. This work proposes a new approach for response extraction in Configurable Ring Oscillator (CRO) PUFs that contributes to reducing area and power consumption by eliminating the need for duplicating Ring Oscillators (ROs) and decreasing associated switching activity. The kernel-based response extraction approach exploits the phase shift of the delay elements and the frequency of ROs to generate unique responses effectively. The extraction approach uses a time-based kernel that enables the internal counters of PUF and evaluates the propagated signals within ROs for a predefined period. The extraction approach has been implemented and verified on a design variant of CRO PUF, which is also proposed within the scope of this work. The proposed PUF has been implemented in 22nm FDSOI technology using Synopsys tools. A comprehensive security analysis has been conducted utilizing Challenge-Response Pairs (CRPs) were collected from 8 chips. The extracted responses achieve an average of 49.42%, 38.25%, 9.95%, and 45.5% for uniformity, diffuseness, reliability, and uniqueness, respectively. Compared with state-of-the-art designs, the proposed design achieves a 75% reduction in area and a 65.1% reduction in power consumption while delivering 2(4n+log2n) CRPs, classified as a strong PUF. Finally, 12 tests of the National Institute of Standards and Technology (NIST) and machine learning modeling are conducted to verify the security of responses. The NIST tests are successfully passed, and the average prediction accuracy of machine learning models is found to be 65.1%.

Publisher

Springer Science and Business Media LLC

Reference25 articles.

1. Al-Meer, A. & Al-Kuwari, S. Physical unclonable functions (puf) for iot devices. ACM Comput. Surv. 55, DOI:

2. 1145/3591464 (2023).

3. A memristor fingerprinting and characterisation methodology for hardware security;Aitchison C;Sci. Reports,2023

4. Designing secure puf-based authentication protocols for constrained environments;Lee S-W;Sci. Reports,2023

5. Huang, Z., Bian, J., Lin, Y., Liang, H. & Ni, T. Design guidelines and feedback structure of ring oscillator puf for performance improvement. IEEE Transactions on Comput. Des. Integr. Circuits Syst. 43, 71–84, DOI: 10.1109/TCAD. 2023.3301386 (2024).

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3