  Oftentimes in practice, the observed process changes statistical properties
at an unknown point in time and the duration of a change is substantially
finite, in which case one says that the change is intermittent or transient. We
provide an overview of existing approaches for intermittent change detection
and advocate in favor of a particular setting driven by the intermittent nature
of the change. We propose a novel optimization criterion that is more
appropriate for many applied areas such as the detection of threats in
physical-computer systems, near-Earth space informatics, epidemiology,
pharmacokinetics, etc. We argue that controlling the local conditional
probability of a false alarm, rather than the familiar average run length to a
false alarm, and maximizing the local conditional probability of detection is a
more reasonable approach versus a traditional quickest change detection
approach that requires minimizing the expected delay to detection. We adopt the
maximum likelihood (ML) approach with respect to the change duration and show
that several commonly used detection rules (CUSUM, window-limited CUSUM, and
FMA) are equivalent to the ML-based stopping times. We discuss how to choose
design parameters for these rules and provide a comprehensive simulation study
to corroborate intuitive expectations.
