Intrusion Detection Using Navie Bayes by Analyzing Big Data

Authors(2) :-B. Geetha Kumari, Jageti Padmavthi

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.

Authors and Affiliations

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

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

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Publication Details

Published in : Volume 2 | Issue 6 | November-December 2017
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 469-474
Manuscript Number : CSEIT1726149
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

B. Geetha Kumari, Jageti Padmavthi, "Intrusion Detection Using Navie Bayes by Analyzing Big Data", International 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.
Journal URL : http://ijsrcseit.com/CSEIT1726149

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