Examination of Double Arbiter PUFs on Security against Machine Learning Attacks

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

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

6 Scopus citations

Abstract

Security is important for the functioning of Internet-of-Things (IoTs). Many IoT devices are closely physically approachable by the crowd, making cryptographic key-based security protocols vulnerable to side-channel attacks. Physical Unclonable Functions (PUF) are emerging as a promising keyless solution by utilizing inherent variations of integrated circuits (ICs) to produce different responses from different devices. While physically unclonable, some PUFs were reported to be mathematically clonable by machine learning (ML) methods. XOR PUFs are a group of PUFs mathematically clonable when a large number of challenge-response pairs (CRPs) are available to attackers. Double Arbiter PUFs (DAPUFs) were developed for increased security against machine learning attacks over XOR PUFs, and studies showed that DAPUFs are highly secure against attacks using Support Vector Machine (SVM). In this paper, we investigate how secure DAPUFs are against neural network-based attack methods and compare the DAPUFs' performance with that of XOR PUFs. The results confirm with existing studies on DAPUFs' higher security when compared with XOR PUFs, but also discovered that DAPUFs are not secure against neural network-based attacks if attackers can obtain a large number of CRPs, revealing a security vulnerability of those DAPUFs we examined in this study.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
EditorsChaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3165-3171
Number of pages7
ISBN (Electronic)9781728108582
DOIs
StatePublished - Dec 2019
Event2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States
Duration: Dec 9 2019Dec 12 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference2019 IEEE International Conference on Big Data, Big Data 2019
Country/TerritoryUnited States
CityLos Angeles
Period12/9/1912/12/19

Keywords

  • Double Arbiter PUF
  • Internet Of Things
  • Machine Learning
  • Physical Unclonable Functions

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