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Bloom Filter is a probabilistic data structure for the membership query, andit has been intensely experimented in various fields to reduce memoryconsumption and enhance a system's performance. Bloom Filter is classified intotwo 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 theissue of the false negative probability. Notably, SBF is also false negativefree, but it cannot support delete operations like CBF. To address theseissues, we present a novel counting Bloom Filter based on SBF and 2D BloomFilter, called countBF. countBF uses a modified murmur hash function to enhanceits various requirements, which is experimentally evaluated. Our experimentalresults show that countBF uses $1.96\times$ and $7.85\times$ less memory thanSBF and CBF respectively, while preserving lower false positive probability andexecution 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.

countBF: A General-purpose High Accuracy and Space Efficient Counting Bloom Filter