A Secure and Efficient Temporal Features Based Framework for Cloud Using MapReduce

Abstract

A new data mining method called temporal pattern identification in cloud is developed using MapReduce: a power full feature of hadoop with temporal features. In the paper a new approach called temporal features based authentication approach has been introduced where a user will be verified by 2 phases. In phase1 the user facial features will be stored at HIB (Hadoop Image Bundle) and in second phase the user credentials will be checked by using symmetric encryption technique. The hadoop cluster can be exported to cloud based on user demand such as IAAS, SAAS, and PAAS. This cloud model is useful to deploy applications that can be use to transmit massive data over cloud environment. It also allows different kinds of optimization techniques and functionalities. The framework can also be used to give optimized security to the massive data stored by user at cloud environment. The article make use of temporal patters of user who entered into cloud by updating it in a logfile. Experimentation conducted by hiding text, image and both in video file and the performance of the cluster is monitored by using efficient monitoring tool called ganglia to provide auto-scaling functionality of the cluster.