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dc.contributor.author Prema, S
dc.contributor.author Asokkumar, S
dc.date.accessioned 2019-08-15T04:48:25Z
dc.date.available 2019-08-15T04:48:25Z
dc.identifier.uri http://dl.lib.mrt.ac.lk/handle/123/14757
dc.description.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. . en_US
dc.language.iso en en_US
dc.subject Big data en_US
dc.subject NIDS en_US
dc.subject NSGA-II en_US
dc.subject Random Forests en_US
dc.subject Spark en_US
dc.subject Hadoop en_US
dc.subject MapReduce en_US
dc.title Nids based random model to protected big data environment using spark en_US
dc.type Conference-Full-text en_US
dc.identifier.faculty other en_US
dc.identifier.year 2019 en_US
dc.identifier.conference International Conference on Business Research en_US
dc.identifier.place Moratuwa en_US
dc.identifier.pgnos 122-132 en_US


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