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EDSAC–An Efficient Dempster Shafer Algorithm for Classification to Estimate the Service, Security and Privacy Risks with the Service Providers

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Abstract

Cloud is an environment where the resources are outsourced as service to the cloud consumers based on their demand. The cloud providers follows pay as you go model for charging the service provided to the cloud consumer. In recent days the number of cloud consumers increases tremendously which results in increase of the cloud providers. Since there exists many numbers of providers in the cloud environment, the cloud consumers are unhinged in selecting an ideal cloud provider for their needs, as they were unaware of risks with them. Hence the proposed work indulges in supporting the cloud consumers for selecting an optimal cloud service provider by analyzing the risks with them. An Efficient Dempster Shafer algorithm for Classification is proposed for analyzing the risks with the cloud service providers. The analysis is based on CAI Questionnaires in which 16 different parameters of CAIQ are classified and reduced to three different risk parameters such as privacy risk, security risk and service risk. Six different providers are analysed where all the three levels of risks are estimated for each providers and are compared to each other based on both total risks at each parameter and overall risk rate of the providers. The accuracy of classification of the proposed algorithm is compared with two other algorithms and found that the proposed one is efficient with 94.6% efficiency.

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References

  1. Mouratidis, H., Islam, S., Kalloniatis, C., & Gritzalis, S. (2013). A framework to support selection of cloud providers based on security and privacy requirements. Journal of Systems and Software, 83, 2276–2293.

    Article  Google Scholar 

  2. Nirnay Ghosh, Soumya K. Ghosh, Sajal K. Das, SelCSP: A Framework to Facilitate Selection of Cloud Service Providers, IEEE Transactions on Cloud Computing, pp. 1–14, 2015.

  3. Wang, Xiaogang, Cao, Jian, & Xiang, Yang. (2014). Dynamic cloud service selection using an adaptive learning mechanism in multi-cloud computing. Journal of Systems and Software, 100, 195–210.

    Article  Google Scholar 

  4. Garg, S. K., Versteeg, S., & Buyya, R. (2013). A framework for ranking of cloud computing services. Future Generation Computer Systems, 29(4), 1012–1023.

    Article  Google Scholar 

  5. Anitha, R. (2014). Saswati Mukherjee, “bloom filter based metadata placement and management in cloud computing. Internationl Journal of Recent Trends in Engineering & Technology, 11, 93–104.

    Article  MathSciNet  Google Scholar 

  6. Supriya, M., Sangeeta, K., & Patra, G. K. (2016). Trustworthy Cloud Service Provider Selection using Multi Criteria Decision Making Methods. Engineering letters, 24, 1–10.

    Google Scholar 

  7. Ma, H., Zhinang, Hu., Li, K., & Zhang, H. (2016). Towards trustworthy cloud service selection. Journal of Parallel and Distributed Computing, 96, 75–94.

    Article  Google Scholar 

  8. Petri, I., & Rana, O. F. (2014). Gheorghe cosmin silaghi, yacine rezgui, “risk assessment in service provider communities.” Future Generation Computer Systems, 41, 32–43.

    Article  Google Scholar 

  9. Rajasree, S., & Lydia Elizabeth, B. (2016). Trust based cloud service provider selection, international journal of engineering and computer. Science, 55, 16708–16713.

    Google Scholar 

  10. Mohammed Alhamad, Tharam Dillon, and Elizabeth Chang, A Trust-Evaluation Metric for Cloud applications, International Journal of Machine Learning and Computing, Vol. 1, No. 4, October 2011.

  11. Amir Karami, Zhiling Guo,A Fuzzy Logic Multi-Criteria Decision Framework for Selecting IT Service Providers, Proc. of45th Hawaii International Conference on System Sciences, pp. 1118–1127, 2012.

  12. Punam Bedi, Harmeet Kaur, Bhavna Gupta, Trustworthy Service Provider Selection in Cloud Computing Environment, Proc. of International Conference on Communication Systems and Network Technologies, pp. 714–719,2012.

  13. Govindarajan, G. Lakshmanan, Overview of cloud standards. In N. Antonopoulos & L. Gillam (Eds.), Cloud computing: Principles, systems and applications,Springer-Verlag, pp. 77–89, 2010.

  14. S. k. Garg, S. Versteeg, R. Buyya, A framework for ranking of cloud computing services. Future Generation Computing Systems, Vol. 29, No. 4,pp. 1012–1023, 2013.

  15. Iosup, S., Ostermann, N., Yigitbasi, R., Prodan, T. Fahringer., & Epema, D. (2011). Performance analysis of cloud computing services for many-tasks scientific computing. IEEE Transactions on Parallel and Distributed Systems, 22(6), 931–945.

    Article  Google Scholar 

  16. Habib, Sheikh Mahbub, Ries, Sebastian, Mühlhäuser, Max, & Varikkattu, Prabhu. (2014). Towards a trust management system for cloud computing marketplaces: using CAIQ as a trust information source. Security and Communication Networks, 7, 2185–2200.

    Article  Google Scholar 

  17. Cayirci, Erdal, Garaga, Alexandr, Santana, Anderson, & de Oliveira and Yves Roudier, . (2016). A risk assessment model for selecting cloud service providers. Journal of Cloud Computing: Advances, Systems and Applications, 5, 1–12.

    Article  Google Scholar 

  18. Saavedra, R. H., & Smith, A. J. (1996). Analysis of benchmark characteristics and benchmark performance prediction. ACM Transactions on Computer Systems, 14(4), 344–384.

    Article  Google Scholar 

  19. Ma, H., Hu, Z., Li, K., & Zhang, H. (2016). Toward trustworthy cloud service selection: A time-aware approach using interval neutrosophic set. Journal of Parallel and Distributed Computing, 96, 75–94.

    Article  Google Scholar 

  20. Hale, M. L., Gamble, R. (2012). Secagreement: Advancing security risk calculations in cloud services, in: Proc. of 2012 IEEE Eighth World Congress on Services (SERVICES), pp. 133–140.

  21. Zeng, L., Veeravalli, B., & Li, X. (2015). SABA: A security-aware and budget-aware workflow scheduling strategy in clouds. Journal of Parallel and Distributed Computing, 75, 141–151.

    Article  Google Scholar 

  22. Alexandru Iosup, Ozan Sonmez, and Dick Epema, DGSim: Comparing Grid Resource Management Architectures through Trace-Based Simulation, Proc. of 14th Int’l Euro-Par Conf. Parallel Processing, pp. 13–25, 2008.

  23. Pawar, P. S., Rajarajan, M., Nair, S. K. & Zisman, A. (2012) Trust Model for Optimized Cloud Services. Trust Management VI, pp. 97–112.

  24. Hua, M. A., & Zhi-gang, H. U. (2015). User preferences-aware recommendation for trustworthy cloud services based on fuzzy clustering. Journal of Central South University, 22(9), 3495–3505.

    Article  Google Scholar 

  25. Iosup and D.H.J. Epema, GrenchMark: A Framework for Analyzing, Testing, and Comparing Grids, Proc. IEEE Sixth Int’l Symp. Cluster Computing and the GRID (CCGrid), pp. 313–320, 2006.

  26. Rehman, Zia Ur, Hussain, Omar Khadeer, & Hussain, Farook Khadeer. (2014). Parallel cloud service selection and ranking based on QoS history. International Journal of Parallel Programming, 42(5), 820–852.

    Article  Google Scholar 

  27. Zheng, Z., Wu, X., Zhang, Y., Lyu, M. R., & Wang, J. (2013). QoS ranking prediction for cloud services. IEEE Transactions on Parallel and Distributed Systems, 24(6), 1213–1222.

    Article  Google Scholar 

  28. Saurabh Kumar Garg, Steve Versteegand Rajkumar Buyya, SMICloud: A Framework for Comparing and Ranking Cloud Services, Proc. of Fourth IEEE International Conference on Utility and Cloud Computing, pp. 210–219, 2012.

  29. Mohammed Alhamad, Tharam Dillon, and Elizabeth Chang, A Trust- Evaluation Metric for Cloud Applications, International Journal of Machine Learning and Computing, Vol. 1, No. 4, 2011.

  30. Wilson, N. (2000). Algorithms for dempster- shafer theory, handbook of defeasible reasoning and uncertainty management systems. Statistical Science, 5, 421–475.

    MathSciNet  Google Scholar 

  31. Martin, R., Zhang, J., & Liu, C. (2010). Dempster-shafer theory and statistical inference with weak beliefs. Statistical Science, 25(1), 72–87.

    Article  MathSciNet  Google Scholar 

  32. Paksoy, A., & Göktürk, M. (2011). Information fusion with dempster-shafer evidence theory for software defect prediction. Procedia Computer Science, 3, 600–605.

    Article  Google Scholar 

  33. Cuzzolin, F. (2012). On the relative belief transform. International Journal of Approximate Reasoning, 53, 786–804.

    Article  MathSciNet  Google Scholar 

  34. Barnett, J. A. (1991). Calculating dempster- shafer plausibility. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(6), 599–602.

    Article  Google Scholar 

  35. Mohammed Ibrahim Almanea, The Role of Transparency and Trust in the Selection of Cloud Service Providers, Thesis, Newcastle University, 2015.

  36. Alexandros Chrysikos, Stephen McGuire, A Predictive Model for Risk and Trust Assessment in Cloud Computing: Taxonomy and Analysis for Attack Pattern Detection, Guide to Vulnarability Analysis for Computer Networks and Systems, pp. 81- 99, 2018.

  37. Alturkistani, Fatimah M., & Emam, Ahmed Z. (2014). A review of security risk assessment methods in cloud computing. New Perspective in Information Systems and Technologies, 1, 443–453.

    Google Scholar 

  38. Hongbing Wang, Feng Liu, Heng Liu, A Method of the Cloud Computing Security Management Risk Assessment. In: Zeng D. (eds) Advances in Computer Science and Engineering. Advances in Intelligent and Soft Computing, vol 141, pp. 609-618, 2012.

  39. Lin, F., Zeng, W., Yang, L., Wang, Y., Lin, S., & Zeng, J. (2017). Cloud Computing system risk estimation and service selection approach based on cloud focus theory. Neural Computing and Applications, 28, 1863–1876.

    Article  Google Scholar 

  40. Martens, B., & Teuteberg, F. (2012). Decicion-making in cloud computing environments: A cost and risk based approach. Information Systems Frontiers, 14(4), 871–893.

    Article  Google Scholar 

  41. ENISA, Cloud Computing: Benefits, risks and recommendations for information security, 2012.

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Acknowledgements

This work is financially supported by Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India, under the Early Career Research Award Scheme. The Grant Number of the project is ECR/2016/000546.

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Correspondence to C. Muralidharan.

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Muralidharan, C., Anitha, R. EDSAC–An Efficient Dempster Shafer Algorithm for Classification to Estimate the Service, Security and Privacy Risks with the Service Providers. Wireless Pers Commun 122, 3649–3669 (2022). https://doi.org/10.1007/s11277-021-09105-8

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  • DOI: https://doi.org/10.1007/s11277-021-09105-8

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