TY - GEN
T1 - Modeling attacks on physical unclonable functions
AU - Rührmair, Ulrich
AU - Sehnke, Frank
AU - Sölter, Jan
AU - Dror, Gideon
AU - Devadas, Srinivas
AU - Schmidhuber, Jürgen
N1 - Generated from Scopus record by KAUST IRTS on 2022-09-14
PY - 2010/12/16
Y1 - 2010/12/16
N2 - We show in this paper how several proposed Physical Unclonable Functions (PUFs) can be broken by numerical modeling attacks. Given a set of challenge-response pairs (CRPs) of a PUF, our attacks construct a computer algorithm which behaves indistinguishably from the original PUF on almost all CRPs. This algorithm can subsequently impersonate the PUF, and can be cloned and distributed arbitrarily. This breaks the security of essentially all applications and protocols that are based on the respective PUF. The PUFs we attacked successfully include standard Arbiter PUFs and Ring Oscillator PUFs of arbitrary sizes, and XOR Arbiter PUFs, Lightweight Secure PUFs, and Feed-Forward Arbiter PUFs of up to a given size and complexity. Our attacks are based upon various machine learning techniques including Logistic Regression and Evolution Strategies. Our work leads to new design requirements for secure electrical PUFs, and will be useful to PUF designers and attackers alike. Copyright 2010 ACM.
AB - We show in this paper how several proposed Physical Unclonable Functions (PUFs) can be broken by numerical modeling attacks. Given a set of challenge-response pairs (CRPs) of a PUF, our attacks construct a computer algorithm which behaves indistinguishably from the original PUF on almost all CRPs. This algorithm can subsequently impersonate the PUF, and can be cloned and distributed arbitrarily. This breaks the security of essentially all applications and protocols that are based on the respective PUF. The PUFs we attacked successfully include standard Arbiter PUFs and Ring Oscillator PUFs of arbitrary sizes, and XOR Arbiter PUFs, Lightweight Secure PUFs, and Feed-Forward Arbiter PUFs of up to a given size and complexity. Our attacks are based upon various machine learning techniques including Logistic Regression and Evolution Strategies. Our work leads to new design requirements for secure electrical PUFs, and will be useful to PUF designers and attackers alike. Copyright 2010 ACM.
UR - http://portal.acm.org/citation.cfm?doid=1866307.1866335
UR - http://www.scopus.com/inward/record.url?scp=78649989155&partnerID=8YFLogxK
U2 - 10.1145/1866307.1866335
DO - 10.1145/1866307.1866335
M3 - Conference contribution
SN - 9781450302449
SP - 237
EP - 249
BT - Proceedings of the ACM Conference on Computer and Communications Security
ER -