A Hadoop Security in Big Data : A Technological Challenges, Opportunity for Analysis In Cloud Computing

Authors

  • Bilal Shabir  M.Tech (Scholar), CSE Depatment Desh Bhagat University, Mandi Gobindgarh, Punjab, India
  • Manoj Kumar Srivastava  CSE Department, Desh Bhagat University, Mandi Gobindgarh, Punjab, India

Keywords:

Amazon Web Services, Securities Protocol, Hadoop Security, Big Data, Hadoop, Kerberos Protocol

Abstract

The world is currently being digitalised. Just like every individual who uses the internet today, a lot of information is generated every day. Data is critical for the conduct of their everyday activities, as well as assisting corporate management in achieving their objectives and making the best judgments possible based on the information acquired. The use of these resources has resulted in amazing massive data advancements, and cloud computing is a critical component in overcoming the challenges of shared computing resources such as computers, storing, networking, and analysis techniques. The Big Data phenomenon is a direct consequence of almost all activities in public, private and commercial life being digitalised and transcribed. Big data, however, generated new issues not only linked to the number or diversity of the information, The Apache Hadoop platform is utilised to manage the data. ut also to the security of data. The safety of such information must be ensured. The Apache Hadoop platform is used for the management, storage, management and distribution of large data through a variety of server nodes. There are various tools here that investigate Apache Hadoop's top layer to ensure data security. To get a thorough overview of the problem, It is decided to do the screening for Apache Hadoop security in large data with the purpose of existing security approaches.

References

  1. Berkovich, S., and D. Liao. 2012. On Clusterization of Big Data Streams. In Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications, 9. ACM. Accessed November 25, 2015. http://portalparts.acm.org/2350000/2345316/fm/frontmatter.pdf?ip = 100.36.182.180&CFID = 791206509&CFTOKEN = 86783680.
  2. Marr, B. 2015. Big Data: Using SMART Big Data. Analytics and Metrics To Make Better Decisions and Improve Performance. Atrium: Wiley.
  3. Slagter, K., C. H. Hsu, and Y. C. Chung. 2015. “An Adaptive and Memory Efficient Sampling Mechanism for Partitioning in MapReduce.” International Journal of Parallel Programming 43 (3): 489–507.
  4. Mayer-Schönberger, V., and K. Cukier. 2013. Big Data: A Revolution That Will Transform How We Live, Work, and Think. Houghton Mifflin Harcourt. (Chapter 1).
  5. Yusuf Perwej, “An Experiential Study of the Big Data,” International Transaction of Electrical and Computer Engineers System (ITECES), USA, ISSN (Print): 2373-1273 ISSN (Online): 2373-1281, Vol. 4, No. 1, page 14-25, March 2017, DOI:10.12691/iteces-4-1-3
  6. V Mayer-Schonberger, K Cukier, Big data: a revolution that will transform how we live work and think, Boston:Houghton Mifflin Harcourt, 2013
  7. Yusuf Perwej, Mahmoud Ahmed AbouGhaly, Bedine Kerim and Hani Ali Mahmoud Harb,“An Extended Review on Internet of Things (IoT) and its Promising Applications”, Communications on Applied Electronics (CAE), ISSN : 2394-4714, Foundation of Computer Science FCS, New York, USA, Volume 9, Number 26, Pages 8– 22, February 2019, DOI: 10.5120/cae2019652812
  8. Yusuf Perwej, Majzoob K. Omer, Osama E. Sheta, Hani Ali M. Harb, Mohmed S. Adrees, “The Future of Internet of Things (IoT) and Its Empowering Technology” , International Journal of Engineering Science and Computing (IJESC), ISSN: 2321- 3361, Volume 9, Issue No.3, Pages 20192– 20203, March 2019
  9. Gartner says 4.9 Billion Connected „Things‟ Will Be in Use in 2015,” Gartner Inc., 2014
  10. Nafus, D. and Sherman, J., 2014. Big data, big questions| this one does not go up to 11: the quantified self movement as an alternative big data practice.. International journal of communication, p. 11.
  11. Bertino, E., P. Bernstein, D. Agrawal, S. Davidson, U. Dayal, M. Franklin, J. Gehrke, et al. 2011. Challenges and Opportunities with Big Data. Accessed November 25, 2015. http://docs.lib.purdue.edu/ccpubs/445/.
  12. Ammn, N., and M. Irfanuddin. 2013. “Big Data Challenges.” International Journal of Advanced Trends in Computer Science and Engineering 2 (1): 613–615.
  13. Bryant, R., R. H. Katz, and E. D. Lazowska. 2008. Big-data Computing: Creating Revolutionary Breakthroughs in Commerce, Science and Society. Accessed November 25, 2015. http://www.datascienceassn.org/sites/default/files/Big%20Data%20Computing%202008%20Paper.pdf.
  14. Ding, J. M., Y. Jiang, Q. X. Wang, Y. L. Liu, and M. J. Li. 2013. “A Data Localization Algorithm for Distributing Column Storage System of Big Data.” Advanced Materials Research 756–759: 3089–3093
  15. Yang, Y., X. Long, and B. Jiang. 2013. “K-Means Method for Grouping in Hybrid MapReduce Cluster.” Journal of Computers 8 (10): 2648–2655
  16. LaValle, S., E. Lesser, R. Shockley, M. S. Hopkins, and N. Kruschwitz. 2013. Big data, Analytics and the Path from Insights to Value. MIT Sloan Management Review, 21. Accessed December 2, 2015. http://sloanreview.mit.edu/ article/big-data-analytics-and-the-path-from-insights-to-value/.
  17. Aghabozorgi, S., A. Seyed Shirkhorshidi, and T. Ying Wah. 2015. “Time-series Clustering – A Decade Review.” Information Systems 53 (C): 16–38
  18. Zhai, Y., Y. S. Ong, and I. W. Tsang. 2014. “The Emerging “Big Dimensionality”.” IEEE Computational Intelligence Magazine 9 (3): 14–26.
  19. Huang, M., and R. Rust. 2013. “IT-Related Service: A Multidisciplinary Perspective.” Journal of Service Research 16 (3): 251–258.
  20. E, Bertino, Carminati B, Ferrari E, Gupta A , and Thuraisingham B. "Selective and Authentic Third- Party Distribution of XML Documents."2004, pp. 1263 - 1278.
  21. Kilzer, Ann, Emmett Witchel, Indrajit Roy, Vitaly Shmatikov, and Srinath T.V. Setty. "Airavat:Security and Privacy for MapReduce."
  22. P.R , Anisha, Kishor Kumar Reddy C, Srinivasulu Reddy K, and Surender Reddy S. "Third Party Data Protection Applied To Cloud and Xacml Implementation in the Hadoop Environment With Sparql."2012. 39-46, Jul – Aug. 2012.
  23. "Security-Enhanced Linux."Security-Enhanced Linux. N.p. Web. 13 Dec 2013
  24. A. Harbitter, D. Menasce, "Perofrmance of public-key-enabled Kerberos authentication in large networks" in Proceedings of 2001 IEEE Symposium on Security and Privacy, IEEE Computer Society Press, 2001.
  25. Kai Zheng , Weihua Jiang,” A token authentication solution for hadoop based on kerberos pre-authentication”, International Conference on Data Science and Advanced Analytics (DSAA), IEEE, Shanghai, China, Nov. 2014.
  26. Charles Schmitt, "Security and Privacy in the Era of Big Data" in RENCI (Renaissance Computing Institute), NCDS, White Paper, 2013
  27. Shuyu Li, Tao Zhang, Jerry Gao, Younghee Park, "A Sticky Policy Framework for Big Data Security", 2015 IEEE First International Conference on Big Data Computing Services and Application, pp. 71, 2015, ISBN 978-1-4799-8128-1/15
  28. J. Kohl, C. Neuman, "The Kerberos Network Authentication Service (V5)", Rfc, pp. 1510, September 1993
  29. S. M. Bellovin, M. Merritt, "Limitations of the kerberos authenication system", Computer Commun. Rev., vol. 20, no. 5, pp. 119-132, Oct. 1990
  30. J. T. Kohl, B. C. Neuman, T. Y. T'so, The evolution of the Kerberos authentication system. Distributed Open Systems, IEEE Computer Society Press, pp. 78-94, 1994
  31. C. Neuman, T. Yu, S. Hartman, K. Raeburn, "The Kerberos network authentication service (V5)", Network Working Group. Request for Comments: 4120, 2005
  32. F. Butler, I. Cervesato, A. D. Jaggard, A. Scedrov, "A formal analysis of some properties of Kerberos 5 using MSR", University of Pennsylvania Department of Computer & Information Science Philadelphia USA Technical Report MS-CIS-04-04, April 2004
  33. William Stallings, "Cryptography and network security principles and practices" in , Pearson Prentice Hall, pp. 401-419, 2006
  34. A. Boldyreva, V. Kumar, "Provable-security analysis of authenticated encryption in Kerberos", IEEE Symposium on Security and Privacy (SP'07), May 2007
  35. S. Sakane, N. Okabey, K. Kamadaz, H. Esakix, "Applying Kerberos to the communication environment for information appliances", Symposium on Applications and the Internet Workshops (IEEE SAINT-w'03), 2003
  36. Qin Li, Fan Yang, Huibiao Zhu, Longfei Zhu, "Formal modeling and analyzing Kerberos protocol", IEEE World Congress on Computer Science and Information Engineering (CSIE) 2009

Downloads

Published

2021-08-30

Issue

Section

Research Articles

How to Cite

[1]
Bilal Shabir, Manoj Kumar Srivastava, " A Hadoop Security in Big Data : A Technological Challenges, Opportunity for Analysis In Cloud Computing, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 8, Issue 4, pp.238-248, July-August-2021.