Abstract

Bloom Filter BF is a simple but powerful data structure that can check membership to a static set The tradeoff to use Bloom filter is a certain configurable risk of false positives The odds of a false positive can be made very low if the hash bitmap is sufficiently large Spam is an irrelevant or inappropriate message sent on the internet to a large number of newsgroups or users A spam word is a list of well-known words that often appear in spam mails The proposed system of Bin Bloom Filter BBF groups the words into number of bins with different false positive rates based on the weights of the spam words An Enhanced Cuckoo Search ECS algorithm is employed to minimize the total membership invalidation cost of the BFs by finding the optimal false positive rates and number of elements stored in every bin The experimental results have demonstrated for CS and ECS for various numbers of bins