A multimodal fusion based framework to reinforce IDS for securing Big Data environment using Spark

摘要

Securing Big Data has become one of the major issues of the exponentially pacing computing world, where data analysis plays an integral role, as it helps data analysts to figure out the interests and detailed information of organizational and industrial assets. Acts like cyber espionage and data theft lead to the inappropriate use of data. In order to detect the malicious content, we propose a model that ensures the security of heterogeneous data residing in commodity hardware. To verify the correctness of our model, (Knowledge Data Discovery) NSL KDD Cup 99 dataset is used that has been used by various researchers for working on (Intrusion Detection System) IDS. We incorporate decision-based majority voting multimodal fusion that combines the results of different classifiers and facilitates better performance in terms of accuracy, detection rate and false alarm rate. Moreover, (Non-dominated Sorting Genetic Algorithm) NSGA-II plays its integral role for the selection of most promising features. Additionally, to reduce the computational complexity which is again a crucial aspect while processing Big Data, we incorporate the concepts of Hadoop MapReduce and Spark to ensure the fast processing of Big Data in a parallel computational environment. Our proposed model is able to achieve 92.03% accuracy, 99.38% detection rate and a testing time of 0.32 seconds. Additionally, we have achieved advantages in terms of accuracy and testing time of data over the existing techniques that use IDS as a security mechanism. (C) 2018 Elsevier Ltd. All rights reserved.