  Physical Unclonable Functions (PUFs) are emerging as promising security
primitives for IoT devices, providing device fingerprints based on physical
characteristics. Despite their strengths, PUFs are vulnerable to machine
learning (ML) attacks, including conventional and reliability-based attacks.
Conventional ML attacks have been effective in revealing vulnerabilities of
many PUFs, and reliability-based ML attacks are more powerful tools that have
detected vulnerabilities of some PUFs that are resistant to conventional ML
attacks. Since reliability-based ML attacks leverage information of PUFs'
unreliability, we were tempted to examine the feasibility of building defense
using reliability enhancing techniques, and have discovered that majority
voting with reasonably high repeats provides effective defense against existing
reliability-based ML attack methods. It is known that majority voting reduces
but does not eliminate unreliability, we are motivated to investigate if new
attack methods exist that can capture the low unreliability of highly but
not-perfectly reliable PUFs, which led to the development of a new reliability
representation and the new representation-enabled attack method that has
experimentally cracked PUFs enhanced with majority voting of high repetitions.
