Does Sophisticating Double Arbiter PUF Design Ensure its Security? Performance and Security Assessments on 5-1 DAPUF

Meznah A. Alamro, Khalid T. Mursi, Yu Zhuang, Mohammed Saeed Alkatheiri, Ahmad O. Aseeri

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Double Arbiter PUFs (DAPUFs) were developed as a variant to XOR PUFs to improve resilience against machine learning attacks. A recent study on DAPUFs of sizes up to 4-1 DAPUFs showed that all examined DAPUFs were vulnerable to machine learning attacks when attackers have access to a large number of challenge-response pairs (CRPs) [1], [10]. In this paper, we implemented the 5-1 DAPUF on field programmable gate arrays (FPGAs), larger than all previously implemented DAPUFs, and carried out performance evaluations of 5-1 DAPUFs on various properties including response randomness, uniqueness, stability, and security vulnerability. Experimental study on 5-1 DAPUFs shows that responses from the same 5-1 DAPUF circuit to different challenges are adequately highly distinguishable from each other while responses generated on different devices to the same challenges are different enough. 5-1 DAPUF also records the highest randomness among all tested sizes of DAPUFs. However, the stability issue is exacerbated in 5-1 DAPUF, a drawback that is also revealed in earlier studies of DAPUFs. Machine learning attack experiments show that 5-1 DAPUF is more resilient than other DAPUFs, but its responses could still be modeled when an attacker is able to accumulate a large number of CRPs.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
EditorsXintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1788-1795
Number of pages8
ISBN (Electronic)9781728162515
DOIs
StatePublished - Dec 10 2020
Event8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States
Duration: Dec 10 2020Dec 13 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference8th IEEE International Conference on Big Data, Big Data 2020
CountryUnited States
CityVirtual, Atlanta
Period12/10/2012/13/20

Keywords

  • Authentication
  • Double Arbiter PUF
  • FPGA
  • Hardware Security
  • Internet of Things
  • Machine Learning
  • Physical Unclonable Functions

Fingerprint Dive into the research topics of 'Does Sophisticating Double Arbiter PUF Design Ensure its Security? Performance and Security Assessments on 5-1 DAPUF'. Together they form a unique fingerprint.

Cite this