Secure Sensitive Services Composition in Edge and Cloud Environment

Authors

  • Khaled Aladham Alenezi College of Computer and Information Sciences Jouf University, KSA
  • Hedi Hamdi College of Computer and Information Sciences Jouf University, KSA and University of Manouba, Manouba, Tunisia

Keywords:

Secure service composition, Edge computing, Cloud computing, Trust level, Majority rating, Internet of Things (IoT), Big data, Cloud security

Abstract

Secure service composition in edge and cloud environments is a challenging task, due to the need to consider factors such as cost, location, sensitivity of the processed data, and trust level. This paper proposes a novel secure service composition approach that addresses these challenges. The proposed approach calculates a weighted rating for each service, based on its rating, trust level, and the sensitivity of the data it will process. The highest weighted rating services are then selected to form the service composition. The proposed approach has been evaluated using a generated dataset of edge and cloud services. The results show that the approach is able to select secure service compositions by considering important factors like: Service rating, Trust level, Sensitivity of the processed data and user needs, by considering all of these factors the proposed approach is able to select secure service compositions that meet the user’s needs while also ensuring the security of the data. The proposed approach can be used to enhance the security of service composition in a range of applications, including cloud computing, business process management, and the Internet of Things. It is particularly useful in edge and cloud environments where sensitive data is processed, this is because the proposed approach takes into account the sensitivity of the data when selecting services. For example, in a cloud-based healthcare application, the proposed approach can be used to select secure services for storing and processing patient data. This can help to protect patient privacy and security. In a Business Process Management workflow for processing financial transactions, the proposed approach can be used to select secure services for authenticating users and authorizing transactions. This can help to prevent fraud and financial loss. In an Internet of Things application for monitoring industrial equipment, the proposed approach can be used to select secure services for collecting and analyzing data from sensors. This can help to protect the industrial equipment from cyberattacks. Overall, the proposed approach is a flexible and effective solution for secure service composition in edge and cloud environments. It can be used to enhance the security of a wide range of applications, particularly those that involve the processing of sensitive data.

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References

Khanouche, M.E., Gadouche, H., Farah, Z. and Tari, A. (2020) Flexible QoS-aware services composition for service computing environments. Comput. Netw., 166, 106982. https://doi.org/10.1016/J.COMNET.2019.106982.

A. Al-Shammari et al., “Secure service composition: A survey,” in Proceedings of the IEEE International Conference on Services Computing, 2015.

Y. Wang, I.-R. Chen, J.-H. Cho, and Jeffrey, “A Comparative Analysis of Trust-based Service Composition Algorithms in Service-Oriented Ad Hoc Networks,” Apr. 2017, doi: https://doi.org/10.1145/3077584.3077590.

Z. Brahmi and A. Selmi, “Coordinate System-Based Trust-Aware Web Services Composition in Edge and Cloud Environment,” The Computer Journal, May 2022, doi: https://doi.org/10.1093/comjnl/bxac063.

J.-J. Guo, J.-F. Ma, X.-X. Guo, X.-H. Li, J.-W. Zhang, and T. Zhang, “Trust-based service composition and selection in service oriented architecture,” Peer-to-Peer Networking and Applications, vol. 11, no. 5, pp. 862–880, Aug. 2017, doi: https://doi.org/10.1007/s12083-017-0593-1.

H. Xie and John, “Mathematical Modeling of Product Rating: Sufficiency, Misbehavior and Aggregation Rules,” arXiv (Cornell University), May 2013, doi: https://doi.org/10.48550/arxiv.1305.1899.

Y. Yan, R. Rosales, G. Fung, R. Subramanian, and J. Dy, “Learning from multiple annotators with varying expertise,” Machine Learning, vol. 95, no. 3, pp. 291–327, Oct. 2013, doi: https://doi.org/10.1007/s10994-013-5412-1.

A. A. Adewuyi, H. Cheng, Q. Shi, J. Cao, X. Wang, and B. Zhou, “SC-TRUST: A Dynamic Model for Trustworthy Service Composition in the Internet of Things,” IEEE Internet of Things Journal, vol. 9, no. 5, pp. 3298–3312, Mar. 2022, doi: https://doi.org/10.1109/jiot.2021.3097980.

P. Wang et al., “Smart Contract-Based Negotiation for Adaptive QoS-Aware Service Composition,” IEEE Transactions on Parallel and Distributed Systems, vol. 30, no. 6, pp. 1403–1420, Jun. 2019, doi: https://doi.org/10.1109/tpds.2018.2885746.

T. Zhang, J. Ma, Q. Li, N. Xi, and C. Sun, “Trust-based service composition in multi-domain environments under time constraint,” Science China Information Sciences, Jul. 2014, doi: https://doi.org/10.1007/s11432-014-5104-x.

P. Wang, X. Liu, J. Chen, Y. Zhan, and Z. Jin, “QoS-aware service composition using blockchain-based smart contracts,” May 2018, doi: https://doi.org/10.1145/3183440.3194978.

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Published

27.12.2023

How to Cite

Alenezi, K. A. ., & Hamdi, H. . (2023). Secure Sensitive Services Composition in Edge and Cloud Environment. International Journal of Intelligent Systems and Applications in Engineering, 12(9s), 01–14. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4141

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Section

Research Article