• DocumentCode
    129518
  • Title

    PUF modeling attacks: An introduction and overview

  • Author

    Ruhrmair, U. ; Solter, Jan

  • Author_Institution
    Tech. Univ. Munchen, München, Germany
  • fYear
    2014
  • fDate
    24-28 March 2014
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Machine learning (ML) based modeling attacks are the currently most relevant and effective attack form for so-called Strong Physical Unclonable Functions (Strong PUFs). We provide an overview of this method in this paper: We discuss (i) the basic conditions under which it is applicable; (ii) the ML algorithms that have been used in this context; (iii) the latest and most advanced results; (iv) the right interpretation of existing results; and (v) possible future research directions.
  • Keywords
    cryptography; learning (artificial intelligence); ML algorithms; ML based modeling attacks; PUF modeling attacks; cryptanalysis; machine learning; strong PUF; strong physical unclonable functions; Delays; Numerical models; Protocols; Security; Stability analysis; Standards; Vectors; Cryptanalysis; Machine Learning; Modeling Attacks; Physical Unclonable Functions;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Design, Automation and Test in Europe Conference and Exhibition (DATE), 2014
  • Conference_Location
    Dresden
  • Type

    conf

  • DOI
    10.7873/DATE.2014.361
  • Filename
    6800562
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=129518