In Internet applications, due to the growth of big data with more features, intrusion detection has become a difficult process in terms of computational complexity, storage efficiency and getting optimized solutions of classification through existing sequential computing environment. Using a parallel computing model and a nature inspired feature selection technique, a Hadoop Based Parallel Binary Bat Algorithm method is proposed for efficient feature selection and classification in order to obtain optimized detection rate. The MapReduce programming model of Hadoop improves computational complexity, the Parallel Binary Bat algorithm optimizes the prominent features selection and parallel Naïve Bayes provide cost-effective classification. The experimental results show that the proposed methodologies perform competently better than sequential computing approaches on massive data and the computational complexity is significantly reduced for feature selection as well as classification in big data applications.
International Journal of Parallel Programming – Springer Journals
Published: Sep 30, 2016
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