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Hadoop Based Parallel Binary Bat Algorithm for Network Intrusion Detection

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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.

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Acknowledgments

The authors would like to thank all anonymous reviewers for their constructive and insightful suggestions to improve this paper.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Erode, Tamil Nadu, 638 052, India

    P. Natesan, R. R. Rajalaxmi & P. Balasubramanie

  2. Department of Electronics and Communication Engineering, Institute of Road and Transport Technology, Erode, Tamil Nadu, 638 316, India

    G. Gowrison

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  1. P. Natesan
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  2. R. R. Rajalaxmi
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  3. G. Gowrison
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  4. P. Balasubramanie
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Correspondence to P. Natesan.

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Natesan, P., Rajalaxmi, R.R., Gowrison, G. et al. Hadoop Based Parallel Binary Bat Algorithm for Network Intrusion Detection. Int J Parallel Prog 45, 1194–1213 (2017). https://doi.org/10.1007/s10766-016-0456-z

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  • DOI: https://doi.org/10.1007/s10766-016-0456-z

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