Hadoop Based Parallel Binary Bat Algorithm for Network Intrusion Detection

Hadoop Based Parallel Binary Bat Algorithm for Network Intrusion Detection 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Parallel Programming Springer Journals

Hadoop Based Parallel Binary Bat Algorithm for Network Intrusion Detection

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Publisher
Springer US
Copyright
Copyright © 2016 by Springer Science+Business Media New York
Subject
Computer Science; Theory of Computation; Processor Architectures; Software Engineering/Programming and Operating Systems
ISSN
0885-7458
eISSN
1573-7640
D.O.I.
10.1007/s10766-016-0456-z
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

International Journal of Parallel ProgrammingSpringer Journals

Published: Sep 30, 2016

References

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