An in-Depth Review on Big Data Intrusion Detection and Optimization on Cloud using Parallel Computation

The large increase in information in the world can be attributed to the increase in the utilization of the internet platform. This steady and monumental increase in the amount, velocity and the variety of the data. This type of data is referred to as the big data, that has high variety, volume and is generated quickly. This data is difficult to process or store in the form of traditional storage mechanisms. Big data has pushed the researchers to develop innovative and resourceful techniques for its analysis as this data can hold extremely insightful knowledge that can be extracted. The big data once stored can be utilized for processing but the conventional analytical techniques are not as effective in realization of this goal. Therefore, there is a need for streamlining of the querying process to make it faster and efficient. Towards this goal the proposed methodology utilizes the parallel computation to achieve effective realization of increased efficiency. The approach also implements bilinear pairing for avalanche effect detection that is used to perform forensic analysis on big data for intrusion detection report generation. This approach will be further expanded in the next version of this research