Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
CRC-PUF: A Machine Learning Attack Resistant Lightweight PUF Construction
KTH, School of Electrical Engineering and Computer Science (EECS), Electronics.ORCID iD: 0000-0001-7382-9408
KTH, School of Electrical Engineering and Computer Science (EECS), Electronics.
KTH, School of Electrical Engineering and Computer Science (EECS), Electronics.
KTH, School of Electrical Engineering and Computer Science (EECS), Electronics.
Show others and affiliations
2019 (English)In: 2019 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), IEEE conference proceedings, 2019, p. 264-271-Conference paper, Published paper (Refereed)
Abstract [en]

Adversarial machine learning is an emerging threat to security of Machine Learning (ML)-based systems. However, we can potentially use it as a weapon against ML-based attacks. In this paper, we focus on protecting Physical Unclonable Functions (PUFs) against ML-based modeling attacks. PUFs are an important cryptographic primitive for secret key generation and challenge-response authentication. However, none of the existing PUF constructions are both ML attack resistant and sufficiently lightweight to fit low-end embedded devices. We present a lightweight PUF construction, CRC-PUF, in which input challenges are de-synchronized from output responses to make a PUF model difficult to learn. The de-synchronization is done by an input transformation based on a Cyclic Redundancy Check (CRC). By changing the CRC generator polynomial for each new response, we assure that success probability of recovering the transformed

Place, publisher, year, edition, pages
IEEE conference proceedings, 2019. p. 264-271-
Keywords [en]
Machine learning, CRC, PUF, hardware security
National Category
Engineering and Technology Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-260434DOI: 10.1109/EuroSPW.2019.00036ISI: 000485315600030Scopus ID: 2-s2.0-85071936707OAI: oai:DiVA.org:kth-260434DiVA, id: diva2:1355593
Conference
IEEE European Symposium on Security and Privacy Workshops
Funder
Vinnova, 2017-05232Vinnova, 2018-03964Swedish Research Council, 2018- 04482
Note

QC 20191001

Available from: 2019-09-30 Created: 2019-09-30 Last updated: 2019-10-01Bibliographically approved

Open Access in DiVA

fulltext(987 kB)15 downloads
File information
File name FULLTEXT01.pdfFile size 987 kBChecksum SHA-512
daf525b5874303510249d4835bb34167609a7ee8034b44586aeb77c008d82f277d8500213e718e04a9117f90cfd012b0c8b7228f0f1060f93a2979eeed2cbb4c
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopusConference webpage

Search in DiVA

By author/editor
Dubrova, ElenaNäslund, OskarDegen, BernhardGawell, AndersYu, Yang
By organisation
Electronics
Engineering and TechnologyElectrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 15 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 19 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf