We introduce the adaptive cuckoo filter (ACF), a data structure for approximate set membership that extends
cuckoo filters by reacting to false positives, removing them for future queries. As an example application,
in packet processing queries may correspond to flow identifiers, so a search for an element is likely to be
followed by repeated searches for that element. Removing false positives can therefore significantly lower
the false-positive rate. The ACF, like the cuckoo filter, uses a cuckoo hash table to store fingerprints. We allow
fingerprint entries to be changed in response to a false positive in a manner designed to minimize the effect
on the performance of the filter. We show that the ACF is able to significantly reduce the false-positive rate
by presenting both a theoretical model for the false-positive rate and simulations using both synthetic data
sets and real packet trace