Nids based random model to protected big data environment using spark

Abstract

Big Data is an active business across the world. With the growing size of data comes many challenges connected with handing out and ensuring the security of huge data. In this paper, we propose a Network Intrusion Detection System (NIDS) model based Random Forests (RF) classifier for anomaly detection of the collected network traffic. In order to decrease the computational time connected with the bulk of captured data, we utilize the system of Hadoop, MapReduce and Spark that have proven to be among the most efficient and fault-tolerant systems. We use the NSL KDD cup 99 dataset to perform experimental analysis and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) for feature selection over this dataset. .