Hybrid Approach of Big Data File Classification Based on Threat Analysis for Enhancing Security

Big Data is rapidly growing domain across various real time areas like Banking, Finance, Indusrty, Medicine, Trading and so on. Due to its diversified application, handling the big data for security during data transmission or management is highly risky. Most of the researchers try to handle big data classification based on the domain of interest for increasing productivity or customer satisfaction in decision making. Whereas, this paper focuses on the classification of big data file to enhance security during the data transmission over network and management.Most of the big data applications contains valuable and confidential data. The existing data security approaches are not sufficient on handling the security for data based on the threat level. Therefore, this paper proposes a hybrid approach to classify the big data based on the threat level of the contents associated with the data under consideration into open and close. To ensure the security of big data files, they are transmitted into the Hadoop Distributed File System along with relevant information to assess the level of threat they pose. The Threat Impact Level (TIL) is then calculated as a metric to determine the threshold level required for their protection.