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PhenoAuth: A Novel PUF-Phenotype-based Authentication Protocol for IoT Devices

Hongming Fei, Owen Millwood, Gope Prosanta, Jack Miskelly, Biplab Sikdar

TL;DR

This work proposes a full noise-tolerant authentication protocol based on the PUF Phenotype concept and methodology for an Internet-of-Things (IoT) network, demonstrating mutual authentication and forward secrecy in a setting suitable for device-to-device communication.

Abstract

Physical Unclonable Functions (PUFs) have been shown to be a highly promising solution for enabling high security systems tailored for low-power devices. Commonly, PUFs are utilised to generate cryptographic keys on-the-fly, replacing the need to store keys in vulnerable, non-volatile memories. Due to the physical nature of PUFs, environmental variations cause noise, manifesting themselves as errors which are apparent in the initial PUF measurements. This necessitates expensive active error correction techniques which can run counter to the goal of lightweight security. ML-based techniques for authenticating noisy PUF measurements were explored as an alternative to error correction techniques, bringing about the concept of a PUF Phenotype, where PUF identity is considered as a structure agnostic representation of the PUF, with relevant noise encoding. This work proposes a full noise-tolerant authentication protocol based on the PUF Phenotype concept and methodology for an Internet-of-Things (IoT) network, demonstrating mutual authentication and forward secrecy in a setting suitable for device-to-device communication. Upon conducting security and performance analyses, it is evident that our proposed scheme demonstrates resilience against various attacks compared to the currently existing PUF protocols.

PhenoAuth: A Novel PUF-Phenotype-based Authentication Protocol for IoT Devices

TL;DR

This work proposes a full noise-tolerant authentication protocol based on the PUF Phenotype concept and methodology for an Internet-of-Things (IoT) network, demonstrating mutual authentication and forward secrecy in a setting suitable for device-to-device communication.

Abstract

Physical Unclonable Functions (PUFs) have been shown to be a highly promising solution for enabling high security systems tailored for low-power devices. Commonly, PUFs are utilised to generate cryptographic keys on-the-fly, replacing the need to store keys in vulnerable, non-volatile memories. Due to the physical nature of PUFs, environmental variations cause noise, manifesting themselves as errors which are apparent in the initial PUF measurements. This necessitates expensive active error correction techniques which can run counter to the goal of lightweight security. ML-based techniques for authenticating noisy PUF measurements were explored as an alternative to error correction techniques, bringing about the concept of a PUF Phenotype, where PUF identity is considered as a structure agnostic representation of the PUF, with relevant noise encoding. This work proposes a full noise-tolerant authentication protocol based on the PUF Phenotype concept and methodology for an Internet-of-Things (IoT) network, demonstrating mutual authentication and forward secrecy in a setting suitable for device-to-device communication. Upon conducting security and performance analyses, it is evident that our proposed scheme demonstrates resilience against various attacks compared to the currently existing PUF protocols.
Paper Structure (23 sections, 2 equations, 6 figures, 3 tables, 3 algorithms)

This paper contains 23 sections, 2 equations, 6 figures, 3 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Motivation and Contributions
  4. Paper organisation
  5. Preliminaries
  6. DRAM PUF
  7. PUF-Phenotype
  8. Reliability Analysis
  9. Proposed Scheme
  10. System Model
  11. Adversary Model and Assumptions
  12. Proposed Authentication Protocol
  13. Enrollment Phase
  14. Authentication Phase:
  15. Security Model and Analysis
  16. ...and 8 more sections

Figures (6)

  • Figure 1: Usage of DRAM PUF in the proposed protocol.
  • Figure 2: System model.
  • Figure 3: The enrollment phase of the proposed protocol
  • Figure 4: Authentication phase of proposed protocol.
  • Figure 5: Security Framework for Mutual Authentication.
  • ...and 1 more figures

Theorems & Definitions (2)

  • Definition 1: Cleanness predicate
  • Definition 2: Matching Session