Big Data Security Tools and Management Practices for Cloud Environment

Abstract

The IT industry has recently grown enamoured with concepts of "cloud computing" (the use of the Internet to store and manage vast amounts of data) and "Big data" (the challenges of dealing with large, unstructured information). This study aspires to demonstrate the efficacy of secure cloud analytics in protecting against prevalent virtualization-based threats such denial-of-service attacks, SQL injections, cross-site scripting attacks, malicious URLs, probes, and user-to-hostname/hostname-to-user lockout attacks. Following the successful introduction of MapReduce based secure analytics for virtualized infrastructure, this study evaluates the effectiveness of utilising up-to-date AES to partition and encrypt cloud-based cubes. Using Hadoop and R-Programming, this experiment uncovered IDS. The second approach included using the Hadoop Framework to detect and simulate complex attacks in a controlled lab environment. Finally, we utilised Cloud Sim to provide a simulation technique to developers of virtual infrastructure and big data, creating a broad variety of VMs that communicate with one another and exhibit unusual behaviour (in the case of certain VMs, we infected them with malware and viruses). Our simulation shows that cloud-based threat detection boosts performance in a wide variety of scenarios.