IDENTIFICATION OF UNSATURATED ATTACKS IN VIRTUALIZED INFRASTRUCTURES WITH BIG DATA ANALYTICS IN CLOUD COMPUTING

Abstract

Security systems to protect virtualized cloud architecture typically include two types of malware detection and security analysis. Detecting malware typically involves two steps, monitoring the hotspots at various points in the virtualized infrastructure, and then using a regularly updated attack signature database to detect the presence of malware. 'Attack. It allows real-time detection of attacks, the use of special signature databases that are vulnerable to zero- day attacks that do not have attack signatures, and therefore traditional infrastructure. cannot detect complex attacks on virtualized infrastructure. Similarly, security analysis eliminates the need for signature databases using event correlation to detect previously undetected attacks, which are often unmanaged, and the current implementation is scalable in nature. In this article, we recommend BDSA's approach to establish a three-tier system for the continuous detection of future attacks. Initially, network logs from the visiting virtual machine and client application logsare sometimes collected from the visiting virtual machines and stored in HDFS. At this point, the strengths of the attack are removed with a connection scheme and a Map Reduce analyzer. Our BDSA approach uses HDFS distribution management and Spark's map-reduction display capability to address security and speed and volume issues.