An Intelligent Pattern Matching approach with Deep Hypersphere Model for Secure Big Data Storage in Cloud Environment

Abstract

Due to the rapid growth of volume, velocity, and diversity of data termed as big data, the traditional data storage systems are inadequate nowadays. There are lots of advancement in the area of storage, processing, and analysis of data. The data are mostly stored in cloud environment and distributed computing frameworks are used to access those data. The distributed data environment in the big data processing and cloud computing imposes many security and privacy challenges. Security measures applied to data storage, analytics and processing. Firewalls are not sufficient to identify content based at-tack. A more efficient algorithm, called as PRS is proposed in the paper, which uses pattern matching technology to identify the intrusion. The working of the algorithm is based on the input data evaluated by a centralized controller based on the previous attack history and attack feature data. The performance analysis of PRS algorithm is compared with the existing pattern matching algorithms such as KMP (Knuth-Morris-Pratt Algorithm) and BM (Boyer-Moore). The simulation result shows the proposed algorithm gives a better and quick attack pattern detection compared to the existing ones. After the PRS (Pattern Recognition for Security) algorithm has identified a matching pattern, the suspected pattern is fed into a deep hypersphere model, which produces a more accurate result for identifying attack patterns.