Like many open-source technologies such as UNIX or TCP/IP, Hadoop was not created with Security in mind. Hadoop however evolved from the other tools over time and got widely adopted across large enterprises. Some of Hadoop’s architectural features present Hadoop its unique security issues. Given this security vulnerability and potential invasion of confidentiality due to malicious attackers or internal customers, organizations face challenges in implementing a strong security framework for Hadoop. Furthermore, given the method in which data is placed in Hadoop Cluster adds to the only growing list of these potential security vulnerabilities. Data privacy is compromised when these critical and data-sensitive blocks are accessed either by unauthorized users or for that matter even misuse by authorized users. In this paper, we intend to address the strategy of data block placement across the allotted DataNodes. Prescriptive analytics algorithms are used to determine the Sensitivity Index of the Data and thereby decide on data placement allocation to provide impenetrable access to an unauthorized user. This data block placement strategy aims to adaptively distribute the data across the cluster using innovative ML techniques to make the data infrastructure extra secured.
An Aggregate MapReduce Data Block Placement Strategy for Wireless IoT Edge Nodes in Smart Grid
