Abstract
Physical Unclonable Functions (PUFs) are known for their unclonability and light-weight design. Recent advancement in technology has significantly compromised the security of PUFs. Machine learning-based attacks have been proven to be able to construct numerical models that predict various types of PUFs with high accuracy with a small set of challenge-response pairs (CRPs). To address the problem, we present a reconfigurable interconnected PUF network (IPN) design that significantly strengthens the security and unclonability of strong PUFs. While the IPN structure itself provides high resilience against modeling attacks, the reconfiguration mechanism remaps the input-output mapping before an attacker could collect sufficient CRPs. Experimental results show that all tested state-of-the-art machine learning attack methods have prediction accuracy of around 50% on a single bit output of a reconfigurable IPN.
Published Version
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