Machine Learning-based Vulnerability Study of Interpose PUFs as Security Primitives for IoT Networks

Bipana Thapaliya, Khalid T. Mursi, Yu Zhuang

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

2 Scopus citations

Abstract

Security is of importance for communication networks, and many network nodes, like sensors and IoT devices, are resource-constrained. Physical Unclonable Functions (PUFs) leverage physical variations of the integrated circuits to produce responses unique to individual circuits and have the potential for delivering security for low-cost networks. But before a PUF can be adopted for security applications, all security vulnerabilities must be discovered. Recently, a new PUF known as Interpose PUF (IPUF) was proposed, which was tested to be secure against reliability-based modeling attacks and machine learning attacks when the attacked IPUF is of small size. A recent study showed IPUFs succumbed to a divide-and-conquer attack, and the attack method requires the position of the interpose bit known to the attacker, a condition that can be easily obfuscated by using a random interpose position. Thus, large IPUFs may still remain secure against all known modeling attacks if the interpose position is unknown to attackers. In this paper, we present a new modeling attack method of IPUFs using multilayer neural networks, and the attack method requires no knowledge of the interpose position. Our attack was tested on simulated IPUFs and silicon IPUFs implemented on FPGAs, and the results showed that many IPUFs which were resilient against existing attacks cannot withstand our new attack method, revealing a new vulnerability of IPUFs by re-defining the boundary between secure and insecure regions in the IPUF parameter space.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Networking, Architecture and Storage, NAS 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728177441
DOIs
StatePublished - 2021
Event15th IEEE International Conference on Networking, Architecture and Storage, NAS 2021 - Riverside, United States
Duration: Oct 24 2021Oct 26 2021

Publication series

Name2021 IEEE International Conference on Networking, Architecture and Storage, NAS 2021 - Proceedings

Conference

Conference15th IEEE International Conference on Networking, Architecture and Storage, NAS 2021
Country/TerritoryUnited States
CityRiverside
Period10/24/2110/26/21

Keywords

  • FPGA
  • Interpose PUF
  • machine learning
  • neural network
  • physical unclonable function

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