In today’s computing era, the world is dealing with big data which has enormously expanded in terms of 7Vs (volume, velocity, veracity, variability, value, variety, visualization). The conventional data structures like arrays, linked list, trees, graphs etc. are not able to effectively handle these big data. Therefore new and dynamic tools and techniques which can handle these big data effectively and efficiently are the need of the hour. This paper aims to provide an enhancement to the recently proposed “dynamic” data structure “r-Train” for handling big data. With the emergence of the “Internet of Things (IoT)” technology, real-time handling of requests and services are pivotal. Therefore it becomes necessary to promptly fetch the required data as and when required from the enormous piles of big data that are generally located at different sites. Therefore an effective searching and retrieval mechanism must be provided that can handle these challenging issues. The primary aim of this proposed refinement is to provide an effective means of insertion, deletion and searching techniques to efficiently handle the big data.

Distributed Denial of Service (DDOS) attacks have become one of the main threats in cloud environment. A DDOS attack can make large scale of damages to resources and access of the resources to genuine cloud users. Old-established defending system cannot be easily applied in cloud computing due to their relatively low competence and wide storage. In this paper we offered a data mining and neural network technique, trained to detect and filter DDOS attacks. For the simulation experiments we used KDD Cup dataset and our lab datasets. Our proposed model requires small storage and ability of fast detection. The obtained results indicate that our model has the ability to detect and filter most type of TCP attacks. Detection accuracy was the metric used to evaluate the performance of our proposed model. From the simulation results, it is visible that our algorithms achieve high detection accuracy (97%) with fewer false alarms.