Intrusion Detection Using Navie Bayes by Analyzing Big Data

Authors

  • B. Geetha Kumari  Assistant Professor, Department of CSE, G. Narayanamma Institute of Technology and Science, Hyderabad, Telangana, India
  • Jageti Padmavthi  Assistant Professor, Department of CSE, G. Narayanamma Institute of Technology and Science, Hyderabad, Telangana, India

Keywords:

Big Data Processing, Data-Driven Model , HACE, WBG Big Data, LHC, IDNB

Abstract

Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-12-31

Issue

Volume 2, Issue 6, November-December-2017

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
B. Geetha Kumari, Jageti Padmavthi, " Intrusion Detection Using Navie Bayes by Analyzing Big Data, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.469-474, November-December-2017.