  The study of regularity in signals can be of great importance, typically in
medicine to analyse electrocardiogram (ECG) or electromyography (EMG) signals,
but also in climate studies, finance or security. In this work we focus on
security primitives such as Physical Unclonable Functions (PUFs) or
Pseudo-Random Number Generators (PRNGs). Such primitives must have a high level
of complexity or entropy in their responses to guarantee enough security for
their applications. There are several ways of assessing the complexity of their
responses, especially in the binary domain. With the development of analog PUFs
such as optical (photonic) PUFs, it would be useful to be able to assess their
complexity in the analog domain when designing them, for example, before
converting analog signals into binary. In this numerical study, we decided to
explore the potential of the disentropy of autocorrelation as a measure of
complexity for security primitives as PUFs or PRNGs with analog output or
responses. We compare this metric to others used to assess regularities in
analog signals such as Approximate Entropy (ApEn) and Fuzzy Entropy (FuzEn). We
show that the disentropy of autocorrelation is able to differentiate between
well-known PRNGs and non-optimised or bad PRNGs in the analog and binary domain
with a better contrast than ApEn and FuzEn. Next, we show that the disentropy
of autocorrelation is able to detect small patterns injected in PUFs responses
and then we applied it to photonic PUFs simulations.
