Title :
PUF modeling attacks: An introduction and overview
Author :
Ruhrmair, U. ; Solter, Jan
Author_Institution :
Tech. Univ. Munchen, München, Germany
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;
Conference_Titel :
Design, Automation and Test in Europe Conference and Exhibition (DATE), 2014
Conference_Location :
Dresden
DOI :
10.7873/DATE.2014.361
