Modelling Delay-based Physically Unclonable Functions through Particle Swarm Optimization

ePrint Report: Modelling Delay-based Physically Unclonable Functions through Particle Swarm Optimization

Nimish Mishra, Kuheli Pratihar, Anirban Chakraborty, Debdeep Mukhopadhyay

Recent advancements in low-cost cryptography have converged upon the use of nanoscale level structural variances as sources of entropy that is unique to each device. Consequently, such delay-based Physically Unclonable Functions or (PUFs) have gained traction for several cryptographic applications. In light of recent machine learning (ML) attacks on delay-based PUFs, the common trend among PUF designers is to either introduce …

applications attacks cost cryptography device entropy eprint report functions low machine machine learning optimization report swarm