CFP last date
20 May 2024
Reseach Article

An Efficient Security for Unstructured Big Data using a Reconfigurable Security Suite

by Parashiva Murthy B.M., Sumithra Devi K.A.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 18
Year of Publication: 2022
Authors: Parashiva Murthy B.M., Sumithra Devi K.A.
10.5120/ijca2022922198

Parashiva Murthy B.M., Sumithra Devi K.A. . An Efficient Security for Unstructured Big Data using a Reconfigurable Security Suite. International Journal of Computer Applications. 184, 18 ( Jun 2022), 42-46. DOI=10.5120/ijca2022922198

@article{ 10.5120/ijca2022922198,
author = { Parashiva Murthy B.M., Sumithra Devi K.A. },
title = { An Efficient Security for Unstructured Big Data using a Reconfigurable Security Suite },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2022 },
volume = { 184 },
number = { 18 },
month = { Jun },
year = { 2022 },
issn = { 0975-8887 },
pages = { 42-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number18/32419-2022922198/ },
doi = { 10.5120/ijca2022922198 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:24:50.728422+05:30
%A Parashiva Murthy B.M.
%A Sumithra Devi K.A.
%T An Efficient Security for Unstructured Big Data using a Reconfigurable Security Suite
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 18
%P 42-46
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The unstructured data security is enhanced using a reconfigurable security suite (RSS). The data node security is improved by seeing categories of data & their levels of sensitivity. The efficiency of the system performance is improved by using classification of data on par with the sensitivity levels. Methods: Adequate security is provided to the unstructured data by bearing in mind the various data nodes & their sensitivity. The proposed reconfigurable security suite effectively classifies the data nodes further into adequate security nodes and also enhances the security system overhead. Finding: performance analysis has been carried out on different data types by considering any one of the parameters in common like service code and sensitive code in different algorithms. The proposed reconfigurable security suite is developed by analysis performance of oracle Exadata and Apache mahout on sensitive, confidential and public data. Novelty: the reconfigurable security suite provides the different types of security services, which include each class of data standards and algorithms. The proposed security suite is developed by considering the mean value of sensitive, confidential and public data nodes etc to identify the security suite overhead.

References
  1. T. -l. Chasupa and W. Paireekreng, "The Framework of Extracting Unstructured Usage for Big Data Platform," 2021 2nd International Conference on Big Data Analytics and Practices (IBDAP), 2021, pp. 90-94, doi: 10.1109/IBDAP52511.2021.9552131.
  2. O. Baker and C. N. Thien, "A New Approach to Use Big Data Tools to Substitute Unstructured Data Warehouse," 2020 IEEE Conference on Big Data and Analytics (ICBDA), 2020, pp. 26-31, doi: 10.1109/ICBDA50157.2020.9289757.
  3. I. Taleb, M. A. Serhani and R. Dssouli, "Big Data Quality Assessment Model for Unstructured Data," 2018 International Conference on Innovations in Information Technology (IIT), 2018, pp. 69-74, doi: 10.1109/INNOVATIONS.2018.8605945.
  4. F. Hamami, I. A. Dahlan, S. W. Prakosa and K. F. Somantri, "Big Data Analytics for Processing Real-time Unstructured Data from CCTV in Traffic Management," 2020 International Conference on Data Science and Its Applications (ICoDSA), 2020, pp. 1-5, doi: 10.1109/ICoDSA50139.2020.9212858.
  5. M. Elsayed, A. Abdelwahab and H. Ahdelkader, "A Proposed Framework for Improving Analysis of Big Unstructured Data in Social Media," 2019 14th International Conference on Computer Engineering and Systems (ICCES), 2019, pp. 61-65, doi: 10.1109/ICCES48960.2019.9068154.
  6. J. McHugh, P. E. Cuddihy, J. W. Williams, K. S. Aggour, V. S. Kumar and V. Mulwad, "Integrated access to big data polystores through a knowledge-driven framework," 2017 IEEE International Conference on Big Data (Big Data), 2017, pp. 1494-1503, doi: 10.1109/BigData.2017.8258083.
  7. K. Ghane, "Big Data Pipeline with ML-Based and Crowd Sourced Dynamically Created and Maintained Columnar Data Warehouse for Structured and Unstructured Big Data," 2020 3rd International Conference on Information and Computer Technologies (ICICT), 2020, pp. 60-67, doi: 10.1109/ICICT50521.2020.00018.
  8. D. Cansell, J. P. Gibson, and D. Méry, “Refinement: A Constructive Approach to Formal Software Design for a Secure e-voting D. Wu, "A big data analytics framework for forecasting rare customer complaints: A use case of predicting MA members' complaints to CMS," 2017 IEEE International Conference on Big Data (Big Data), 2017, pp. 3965-3967, doi: 10.1109/BigData.2017.8258406. Interface,” Electron. Notes Theor. Comput. Sci., vol. 183, pp. 39–55, Jul. 2007, doi: 10.1016/j.entcs.2007.01.060.
  9. L. Xianglan, "Digital construction of coal mine big data for different platforms based on life cycle," 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), 2017, pp. 456-459, doi: 10.1109/ICBDA.2017.8078862..
  10. Y. Cui, S. Kara and K. C. Chan, "Monitoring and Control of Unstructured Manufacturing Big Data," 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2020, pp. 928-932, doi: 10.1109/IEEM45057.2020.9309975.
  11. K. Adnan, R. Akbar and K. S. Wang, "Towards Improved Data Analytics Through Usability Enhancement of Unstructured Big Data," 2021 International Conference on Computer & Information Sciences (ICCOINS), 2021, pp. 1-6, doi: 10.1109/ICCOINS49721.2021.9497187.
  12. S. Yadav, G. Kumar and S. Kumar, "A graph construction study for graph-based semi-supervised learning: Case study on unstructured text data," 2019 IEEE International Conference on Big Data (Big Data), 2019, pp. 6254-6256, doi: 10.1109/BigData47090.2019.9006465.
  13. Shivaji, R., Nataraj, K.R., Mallikarjunaswamy, S., Rekha, K.R. (2022). Implementation of an Effective Hybrid Partial Transmit Sequence Model for Peak to Average Power Ratio in MIMO OFDM System. ICDSMLA 2020. Lecture Notes in Electrical Engineering, vol 783. Springer, Singapore. https://doi.org/10.1007/978-981-16-3690-5_129.
  14. Mallikarjunaswamy, S., Sharmila, N., Siddesh, G.K., Nataraj, K.R., Komala, M. (2022). A Novel Architecture for Cluster Based False Data Injection Attack Detection and Location Identification in Smart Grid. In: Mahanta, P., Kalita, P., Paul, A., Banerjee, A. (eds) Advances in Thermofluids and Renewable Energy . Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-3497-0_48
  15. Mallikarjunaswamy, S., Sharmila, N., Siddesh, G.K., Nataraj, K.R., Komala, M. (2022). A Novel Architecture for Cluster Based False Data Injection Attack Detection and Location Identification in Smart Grid.Advances in Thermofluids and Renewable Energy . Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-3497-0_48..
  16. X. Ge, X. Zhang and P. K. Chrysanthis, "ExNav: An Interactive Big Data Exploration Framework for Big Unstructured Data," 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 503-512, doi: 10.1109/BigData50022.2020.9377741.
  17. L. Yao et al., "Index Method of Unstructured Data in Power System Based on Improved B+ Tree," 2021 International Conference on Wireless Communications and Smart Grid (ICWCSG), 2021, pp. 574-577, doi: 10.1109/ICWCSG53609.2021.00122.
  18. X. Deng, "Big data technology and ethics considerations in customer behavior and customer feedback mining," 2017 IEEE International Conference on Big Data (Big Data), 2017, pp. 3924-3927, doi: 10.1109/BigData.2017.8258399..
  19. Manjunath T. N., Mallikarjunaswamy S, " An efficient hybrid reconfigurable wind gas turbine power management system using MPPT algorithm,"2021,pp 2501-2510, doi:10.11591/ijpeds.v12.i4.pp 2501-2510.
  20. Mallikarjunaswamy, S., Nataraj, K.R., Rekha, K.R. (2014). Design of High-Speed Reconfigurable Coprocessor for Next-Generation Communication Platform. Emerging Research in Electronics, Computer Science and Technology. Lecture Notes in Electrical Engineering, vol 248. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1157-0_7.
  21. M. Lokanan, "Coding and Analytical Problems with Big Data When Conducting Research on Financial Crimes," 2018 IEEE International Conference on Big Data (Big Data), 2018, pp. 5386-5388, doi: 10.1109/BigData.2018.8621976.
  22. S. Awaghad, "SCEM: Smart & effective crowd management with a novel scheme of big data analytics," 2016 IEEE International Conference on Big Data (Big Data), 2016, pp. 2000-2003, doi: 10.1109/BigData.2016.7840822.
  23. S. K. Sahu, M. M. Jacintha and A. P. Singh, "Comparative study of tools for big data analytics: An analytical study," 2017 International Conference on Computing, Communication and Automation (ICCCA), 2017, pp. 37-41, doi: 10.1109/CCAA.2017.8229827.
  24. Q. Tan, "Research on E-Commerce Security and Data Analysis Platform in the Era of Big Data," 2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), 2020, pp. 410-414, doi: 10.1109/MLBDBI51377.2020.00087.
  25. M. Kantarcioglu and F. Shaon, "Securing Big Data in the Age of AI," 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), 2019, pp. 218-220, doi: 10.1109/TPS-ISA48467.2019. 00035.
Index Terms

Computer Science
Information Sciences

Keywords

Big Data Oracle Exadata Apache Mahout Reconfigurable security suite