  Bloom Filter is a probabilistic data structure for the membership query, and
it has been intensely experimented in various fields to reduce memory
consumption and enhance a system's performance. Bloom Filter is classified into
two key categories: counting Bloom Filter (CBF), and non-counting Bloom Filter.
CBF has a higher false positive probability than standard Bloom Filter (SBF),
i.e., CBF uses a higher memory footprint than SBF. But CBF can address the
issue of the false negative probability. Notably, SBF is also false negative
free, but it cannot support delete operations like CBF. To address these
issues, we present a novel counting Bloom Filter based on SBF and 2D Bloom
Filter, called countBF. countBF uses a modified murmur hash function to enhance
its various requirements, which is experimentally evaluated. Our experimental
results show that countBF uses $1.96\times$ and $7.85\times$ less memory than
SBF and CBF respectively, while preserving lower false positive probability and
execution time than both SBF and CBF. The overall accuracy of countBF is
$99.999921$, and it proves the superiority of countBF over SBF and CBF. Also,
we compare with other state-of-the-art counting Bloom Filters.
