KMDT: A Hybrid Cluster Approach for Anomaly Detection Using Big Data

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 701)

Abstract

In the current digital era, huge data are being generated in a voluminous state from different sources. This lead towards a processing repository called Big Data. Managing and processing such data in parallel clusters is a big challenge. To capture this problem, in this paper, we propose a hybrid algorithm for cluster analysis using the Spark framework for analyzing the Big Data instances. The proposed algorithm is the combination of two machine learning techniques namely, K-Means (KM) and C5.0 Decision Tree (DT). As per the factor of cluster, euclidean distance is used to find the nearest cluster and the related DT is built for each cluster using C5.0 DT algorithm. The inferences of the DT are used to classify each anomaly and the normal instances of the large datasets. Experimental results show that the proposed hybrid algorithm outperforms with other existing algorithms and produces better classification accuracy for anomaly detection.

Keywords

Hadoop Spark K-means Decision tree Big Data 

Notes

Acknowledgements

This work is partially supported by Indian Institute of Technology (ISM), Govt. of India. The authors wish to express their gratitude and thanks to the Department of Computer Science and Engineering, Indian Institute of Technology (ISM), Dhanbad, India for providing their support in arranging necessary computing facilities.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology (ISM)DhanbadIndia

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