Enriching Big Data Intrusion Detection and Service Through Mapping and Parallel Computation

Large-scale analysis of data requires a significant amount of computing power and resources, so the vast and flexible interpreting, preservation, and remote servers that cloud computing companies provide are very attractive. MongoDB is a widely used database management system for big data that is known for its fast performance and ability to execute activities within the MapReduce framework. The notion of big data relies on the conceptualization of resource collaboration, where applications and related infrastructure are frequently distributed among multiple users or organizations through isolation techniques such as virtualization. Big data is vulnerable to various types of assaults owing to its collaborative assets and extensive system software. The lack of efficacy and utility in improving the efficiency and privacy of big data is a significant challenge. The mission of securing big data is of utmost importance, as any discrepancies in this regard can have significant detrimental effects. The complex and voluminous characteristics of big data pose significant challenges in the application of traditional security methodologies, thereby leading to potential issues. Therefore, this research approach utilizes effective big data query classification and partitioning along with mapping and parallel computation for improving efficiency, as well as a bilinear pairing, avalanche effect, and tamper detection for forensic analysis and report generation. The approach has been effectively tested and compared with conventional approaches to achieve improved outcomes.