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International Journal of Parallel Programming
Erschienen in: International Journal of Parallel Programming 5/2017

30.09.2016

Hadoop Based Parallel Binary Bat Algorithm for Network Intrusion Detection

verfasst von: P. Natesan, R. R. Rajalaxmi, G. Gowrison, P. Balasubramanie

Erschienen in: International Journal of Parallel Programming | Ausgabe 5/2017

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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.
Vorheriger Artikel An Approach to Forecast Queue Time in Adaptive Scheduling: How to Mediate System Efficiency and Users Satisfaction
Nächster Artikel A Decomposition of the Tikhonov Regularization Functional Oriented to Exploit Hybrid Multilevel Parallelism

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Metadaten
Titel
Hadoop Based Parallel Binary Bat Algorithm for Network Intrusion Detection
verfasst von
P. Natesan
R. R. Rajalaxmi
G. Gowrison
P. Balasubramanie
Publikationsdatum
30.09.2016
Verlag
Springer US
Erschienen in
International Journal of Parallel Programming / Ausgabe 5/2017
Print ISSN: 0885-7458
Elektronische ISSN: 1573-7640
DOI
https://doi.org/10.1007/s10766-016-0456-z

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