Statistical Analysis of Threat Detection Framework for Securing Semantic Web Amenities

Authors

  • Nagendra Kumar Singh Nill Author
  • Sandeep Kumar Nayak Nill Author
  • Mohammad Faisal Nill Author

DOI:

https://doi.org/10.61841/xrf0x097

Keywords:

Access Control, Dispersed database ploy, Digital-trade, Semantic Web, Chi-square test

Abstract

The emergence of semantic web amenities has momentously streamlined accustomed life. Semantic web amenities have emerged as a medium through which conformist people can access various types of amenities quite easily. Digital trade is an emerging semantic web service that millions of users use in daily life. Through these amenities, the user acquires various types of information, as well as shares his confidential information. These amenities use assortments of database programs that preserve disparate types of data. That is why it is necessary that assorted types of database programs are used instead of the one type of database program to preserve disparate types of data available on semantic Web amenities. In this research paper, the utility of various types of database programs to preserve different types of data is explained through statistical analysis. In this paper, the chi-square test is used for statistical analysis. This paper shows how a single database program is not relevant to preserving different types of data and also shows how different types of database programs are capable of unprecedented increases in the functionality of semantic web amenities.

 

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References

1. Martínez-González, M.M. and Alvite-Díez, M.L., 2019. Thesauri and Semantic Web: Discussion of the evolution of thesauri toward their integration with the Semantic Web. IEEE Access, 7, pp.153151-153170.

2. Antoniazzi, F. and Viola, F., 2019. Building the Semantic Web of Things Through a Dynamic Ontology. IEEE Internet of Things Journal, 6(6), pp.10560-10579.

3. Singh, N.K. and Nayak, S.K., 2019. A Framework for Integrated Threat Detection on Semantic Web Services. International Journal of International Journal of Control and Automation, Vol. 12, No. 4, pp. 157-169.

4. Sharma, R. and Kumar, M., 2014, July. Validation of the data warehouse metrics using formal frameworks. In 2014 International Conference on Signal Propagation and Computer Technology (ICSPCT 2014) (pp. 239-243). IEEE.

5. Tao, Y., Ding, L. and Ganz, A., 2017. Indoor navigation validation framework for visually impaired users. IEEE Access, 5, pp.21763-21773.

6. Guapo, F., Correia, P., Meuwly, D. and van der Vloed, D., 2016, March. Empirical validation of likelihood ratio methods–A case study in forensic speaker recognition. In 2016 4th International Conference on Biometrics and Forensics (IWBF) (pp. 1-5). IEEE.

7. Tantithamthavorn, C., McIntosh, S., Hassan, A.E. and Matsumoto, K., 2016. An empirical comparison of model validation techniques for defect prediction models. IEEE Transactions on Software Engineering, 43(1), pp.1-18.

8. Cunha, J., Fernandes, J.P., Mendes, J. and Saraiva, J., 2014. Embedding, evolution, and validation of model-driven spreadsheets. IEEE Transactions on software Engineering, 41(3), pp.241-263.

9. Badhalia Gems [Online], Available: http://jaipur.dkinfosolutions.com/badhaliagems/

10. Singh, N.K. and Nayak, S.K., 2019. The Threat Detection Framework for Securing Semantic Web Services. Journal of Computational and Theoretical Nanoscience, 16(12), pp.5099-5104.

11. RStudio, https://www.rstudio.com/about

12. R Language, https://www.r-project.org/about.html

13. Chisq.test(), https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/chisq.test

14. Singh, N.K. and Nayak, S.K., 2019. Semantic Web Services: An Empirical Methodology to Security. International Journal of Engineering and Advanced Technology (IJEAT), 8(6), pp. (3100-3104)

15. Singh, N.K. and Nayak, S.K., 2019. A Pragmatic Algorithm for Attack Prevention on Semantic Web Services. International Journal of Engineering and Advanced Technology (IJEAT), 9(1), pp. (1945-1950).

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Published

31.10.2020

Issue

Vol. 24 No. 8 (2020): 24 (8) 2020

Section

Articles

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How to Cite

Singh, N. K., Nayak, S. K., & Faisal, M. (2020). Statistical Analysis of Threat Detection Framework for Securing Semantic Web Amenities. International Journal of Psychosocial Rehabilitation, 24(8), 8295-8306. https://doi.org/10.61841/xrf0x097