A space-and-time efficient technique for big data security analytics

The increase of mobile applications and social media are daily generating huge volumes of data. The variety of this data shapes an evolving term known as big data. Efficiently handling the big data seems a challenge to meet the rate of data growth. This challenge has played a significant role not only for indexing, but also for correlating events across space and time of big data security analytics. Therefore, this paper introduces a Bloom filter (BF) which is a space-and-time efficient probabilistic technique. The BF and its variants are summarized in terms of their contributions to the network security domain. Besides, these variants are evaluated against the characteristics of big data security analytics. By conducting an experiment with a huge volume of data, the introduced technique along with an unsupervised learning engine is tested. The results showed that the BF can be used to overcome the efficiency lacking in the space-and-time of both indexing and analyzing big data.