A Subspace Pre-learning Approach to Fast High-Accuracy Machine Learning of Large XOR PUFs with Component-Differential Challenges

Ahmad O. Aseeri, Yu Zhuang, Mohammed Saeed Alkatheiri

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

3 Scopus citations

Abstract

Physical Unclonable Functions (PUFs), leveraging integrated circuits' manufacturing variations to produce responses unique for individual devices, are emerging as a promising class of security hardware primitives. Implementable with simplistic circuits and requiring low operation energy, PUFs are particularly suitable for resource-constrained systems. An important part of security research is to discover all possible security risks. Such information is useful for PUF developers to design new PUFs to overcome existing risks as well as for PUF-utilizing application developers to avoid vulnerable PUFs. While physically unclonable, some PUFs have been found to be mathematically clonable by machine learning methods which can accurately predict the responses of PUFs. Mathematical clonability allows attackers to develop malicious software to impersonate PUF-embedded devices by producing the same responses PUFs would give. Existing studies on machine learning attack of PUFs have not found vulnerability of large XOR PUFs with component-differential challenges. We believe that the high dimensionality of the challenge space of such PUFs is the underlying reason for the difficulty of machine learning attacks. In this paper, we introduce a PUF-architecture-tailored subspace prelearning-based attack method that can learn the responses of such XOR PUFs fast and accurately, revealing a vulnerability of these XOR PUFs if the PUF has an interface conforming to the way challenge-response data are accessed for the subspace prelearning-based attack method.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
EditorsYang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1563-1568
Number of pages6
ISBN (Electronic)9781538650356
DOIs
StatePublished - Jan 22 2019
Event2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
Duration: Dec 10 2018Dec 13 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

Conference

Conference2018 IEEE International Conference on Big Data, Big Data 2018
Country/TerritoryUnited States
CitySeattle
Period12/10/1812/13/18

Keywords

  • Internet of Things
  • Machine Learning
  • Physical Unclonable Functions
  • Security Vulnerability

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