research-article

A Shared Two-way Cybersecurity Model for Enhancing Cloud Service Sharing for Distributed User Applications

Author:

Yasser D. Al-OtaibiAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 22, Issue 2

Article No.: 47, Pages 1 - 17

https://doi.org/10.1145/3430508

Published: 22 October 2021 Publication History

Abstract

Cloud services provide decentralized and pervasive access for resources to reduce the complex infrastructure requirements of the user. In decentralized service access, the implication of security is tedious to match the user requirements. Therefore, cloud services incorporate cybersecurity measures for administering standard resource access to users. In this paper, a shared two-way security model (STSM) is proposed to provide adaptable service security for the end-users. In this security model, a cooperative closed access session for information sharing between the cloud and end-user is designed with the help of cybersecurity features. This closed access provides less complex authentication for users and data that is capable of matching the verifications of the cloud services. A deep belief learning algorithm is used to differentiate the cooperative and non-cooperative secure sessions between the users and the cloud to ensure closed access throughout the data sharing time. The output of the belief network decides the actual session time between the user and the cloud, improving the span of the sharing session. Besides, the proposed model reduces false alarm, communication failures, under controlled complexity.

References

[1]

C. Choi and J. Choi. 2019. Ontology-based security context reasoning for power IoT-Cloud security service. IEEE Access 7 (2019), 110510–110517.

[2]

T. Halabi and M. Bellaiche. 2018. A broker-based framework for standardization and management of Cloud security-SLAs. Computers & Security 75 (2018), 59–71.

[3]

C. Lei, H. Dai, Z. Yu, and R. Li. 2020. A service recommendation algorithm with the transfer learning based matrix factorization to improve cloud security. Information Sciences 513 (2020), 98–111.

Digital Library

[4]

Z. Xi et al. 2019. Research on underwater wireless sensor network and MAC protocol and location algorithm. In IEEE Access 7 (2019), 56606–56616.

[5]

P. S. Febin Sheron et al. 2019. A decentralized scalable security framework for end-to-end authentication of future IoT communication. Transactions on Emerging Telecommunications Technologies (2019): e3815.

[6]

S. Rizvi, J. Ryoo, J. Kissell, W. Aiken, and Y. Liu. 2017. A security evaluation framework for cloud security auditing. The Journal of Supercomputing 74, 11 (2017), 5774–5796.

Digital Library

[7]

S. S. L. Preeth, R. Dhanalakshmi, R. Kumar, and P. M. Shakeel. 2018. An adaptive fuzzy rule based energy efficient clustering and immune-inspired routing protocol for WSN-assisted IoT system. Journal of Ambient Intelligence and Humanized Computing 1–13.

[8]

C. Thota, R. Sundarasekar, G. Manogaran, R. Varatharajan, and M. K. Priyan. Centralized fog computing security platform for IoT and cloud in healthcare system. In Exploring the Convergence of Big Data and the Internet of Things. IGI Global, USA.

[9]

S. Lins, S. Schneider, and A. Sunyaev. 2018. Trust is good, control is better: Creating secure clouds by continuous auditing. IEEE Transactions on Cloud Computing 6, 3 (2018), 890–903.

[10]

A. Furfaro, A. Piccolo, A. Parise, L. Argento, and D. Saccà. 2018. A Cloud-based platform for the emulation of complex cybersecurity scenarios. Future Generation Computer Systems 89 (2018), 791–803.

[11]

D. He, N. Kumar, S. Zeadally, A. Vinel, L. T. Yang. 2017. Efficient and privacy-preserving data aggregation scheme for smart grid against internal adversaries. IEEE Transactions on Smart Grid 8, 5, (2017), 2411–2419. DOI:https://doi.org/10.1109/TSG.2017.2720159

[12]

D. G. Arce. 2020. Cybersecurity and platform competition in the cloud. Computers & Security 93 (2020), p. 101774.

[13]

O. O. Malomo, D. B. Rawat, and M. Garuba. 2018. Next-generation cybersecurity through a blockchain-enabled federated cloud framework. The Journal of Supercomputing 74, 10 (2018), 5099–5126.

Digital Library

[14]

J. K. Lee, Y. Chang, H. Y. Kwon, and B. Kim. 2020. Reconciliation of privacy with preventive cybersecurity: The bright internet approach. Information Systems Frontiers 22, 1 (2020), 45–57.

[15]

Subramani Jegadeesan, Maria Azees, Priyan Malarvizhi Kumar, Gunasekaran Manogaran, Naveen Chilamkurti, R. Varatharajan, and Ching-Hsien Hsu. 2019. An efficient anonymous mutual authentication technique for providing secure communication in mobile cloud computing for smart city applications. Sustainable Cities and Society 49, (2019), 101522.

[16]

M. Ficco and F. Palmieri. 2019. Leaf: An open-source cybersecurity training platform for realistic edge-IoT scenarios. Journal of Systems Architecture 97 (2019), 107–129.

Digital Library

[17]

Ashish Ghosh, Debasrita Chakraborty, and Anwesha Law. 2018. Artificial intelligence in Internet of Things. CAAI Transactions on Intelligence Technology 3, 4, (2018), 208–218. https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/trit.2018.1008.

Digital Library

[18]

T.-Y. Lee, Y.-J. Chen, and G.-J. Horng. 2019. Forming high-availability cloud mechanism for secure peer-servicing networks. Wireless Personal Communications 109, 1 (2019), 361–391.

Digital Library

[19]

A. A. Alli and M. M. Alam. 2019. SecOFF-FCIoT: Machine learning based secure offloading in Fog-Cloud of Things for smart city applications. Internet of Things 7 (2019), p. 100070.

[20]

H. Jia, X. Liu, X. Di, H. Qi, L. Cong, J. Li, and H. Yang. 2019. Security strategy for virtual machine allocation in cloud computing. Procedia Computer Science 147 (2019), 140–144.

Digital Library

[21]

W. Li, J. Cao, K. Hu, J. Xu, and R. Buyya. 2019. A trust-based agent learning model for service composition in mobile cloud computing environments. IEEE Access 7 (2019), 34207–34226.

[22]

M. Kazim, L. Liu, and S. Y. Zhu. 2018. A framework for orchestrating secure and dynamic access of IoT services in multi-cloud environments. IEEE Access 6 (2018), 58619–58633.

[23]

F. Kong, Y. Zhou, B. Xia, L. Pan, and L. Zhu. 2019. A security reputation model for IoT health data using S-AlexNet and dynamic game theory in cloud computing environment. IEEE Access 7 (2019), 161822–161830.

[24]

J. Li, M. Wen, and K. Zhang. 2020. Cloud-assisted secure and conjunctive publish/subscribe service in smart grids. IET Information Security 14, 4 (2020), 470–481.

[25]

X. Li, Q. Wang, X. Lan, X. Chen, N. Zhang, and D. Chen. 2019. Enhancing cloud-based IoT security through trustworthy cloud service: An integration of security and reputation approach. IEEE Access 7 (2019), 9368–9383.

[26]

Q. Jiang, J. Ma, and F. Wei. 2016. On the security of a privacy-aware authentication scheme for distributed mobile cloud computing services. IEEE Systems Journal 12, 2 (2016), 2039–2042.

[27]

J. Wang, M. Neil, and N. Fenton. 2020. A Bayesian network approach for cybersecurity risk assessment implementing and extending the FAIR model. Computers & Security 89 (2020), p. 101659.

Digital Library

[28]

F. Amato, N. Mazzocca, and F. Moscato. 2018. Model driven design and evaluation of security level in orchestrated cloud services. Journal of Network and Computer Applications 106 (2018), 78–89.

[29]

M. Elsayed and M. Zulkernine. 2019. Offering security diagnosis as a service for cloud SaaS applications. Journal of Information Security and Applications 44 (2019), 32–48.

[30]

A. Celesti, M. Fazio, A. Galletta, L. Carnevale, J. Wan, and M. Villari. 2019. An approach for the secure management of hybrid cloud–edge environments. Future Generation Computer Systems 90 (2019), 1–19.

[31]

Y. Gahi and I. E. Alaoui. 2019. A secure multi-user database-as-a-service approach for cloud computing privacy. Procedia Computer Science 160 (2019), 811–818.

[32]

X. Liu, C. Xia, T. Wang, L. Zhong, and X. Li. 2020. A behavior-aware SLA-based framework for guaranteeing the security conformance of cloud service. Frontiers of Computer Science 14, 6 (2020).

Digital Library

[33]

H. Hammami, S. B. Yahia, and M. S. Obaidat. 2020. A lightweight anonymous authentication scheme for secure cloud computing services. The Journal of Supercomputing 2020.

[34]

S. Park, H. Kim, J. Park, and J. Ryou. 2018. Utilizing a lightweight PKI mechanism to guarantee a secure service in a cloud environment 2018.

Digital Library

[35]

W.-K. Lee, R. C.-W. Phan, G.-S. Poh, and B.-M. Goi. 2017. SearchaStore: Fast and secure searchable cloud services. Cluster Computing 21, 2 (2017), 1189–1202.

[36]

A. K. Bashir, R. Arul, R. Jayaram, A. Arulappan, and S. B. Prathiba. 2019. An optimal multitier resource allocation of cloud RAN in 5G using machine learning. Transactions on Emerging Telecommunications Technologies, Wiley 2019.

Digital Library

[37]

M. Shafiq, X. Yu, A. K. Bashir, H. N. Chuahdry, and D. Wang. 2018. A machine learning approach for feature selection traffic classification using security analysis. Journal of Supercomputing, Springer 76 (2018), 4867–4892.

Digital Library

[38]

G. Raja, S. Anbalagan, G. Vijayaraghavan, P. Dhanasekaran, Y. D. Al-Otaibi, and A. K. Bashir. 2020. Energy-efficient end-to-end security for software defined vehicular networks. IEEE Transactions on Industrial Informatics 2020.

[39]

M. Shafiq, Z. Tian, A. K. Bashir, X. Du, and M. Guizani. 2020. Iot malicious traffic identification using wrapper-based feature selection mechanisms. computers & security. Elsevier 2020.

Cited By

Clement NArce D(2024)Dynamics of Shared Security in the CloudInformation Systems Research10.1287/isre.2023.0256Online publication date: 29-Jul-2024
https://doi.org/10.1287/isre.2023.0256
An LQiu JZhang HLiu C(2024)Design of distributed network intrusion prevention system based on Spark and P2DR modelsCluster Computing10.1007/s10586-024-04487-327:8(10757-10776)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s10586-024-04487-3
ÇAY AKASAS DEREL-OZCEVİK M(2022)Attendance as a Service: A multi-layered system design in attendance applications for Covid-19Celal Bayar Üniversitesi Fen Bilimleri Dergisi10.18466/cbayarfbe.102824218:3(265-273)Online publication date: 29-Sep-2022
https://doi.org/10.18466/cbayarfbe.1028242

Index Terms

A Shared Two-way Cybersecurity Model for Enhancing Cloud Service Sharing for Distributed User Applications
1. Networks
  1. Network services
    1. Cloud computing
    2. In-network processing
2. Security and privacy
  1. Network security
    1. Mobile and wireless security
    2. Security protocols

Recommendations

Network service sharing infrastructure: service authentication and authorization revocation
ICCOM'05: Proceedings of the 9th WSEAS International Conference on Communications

When a service server receives a request, the server will establish the identity and authorization of the user based on the information stored in authentication information repository (IDAR) before service is provided. The IDAR will determine who can ...
Analysis of Security Vulnerabilities of Cloud Computing Environment Service Models and Its Main Characteristics
ITNG '15: Proceedings of the 2015 12th International Conference on Information Technology - New Generations

We are currently observing a huge paradigm shift by different organizations moving their information technology services from enterprise-based data centers that run on different dedicated servers on their premises to cloud computing environment. ...
Analysis of modern cloud services to ensure cybersecurity
Abstract
This article describes the use of cloud services. The analysis of the most popular cloud services and comparison of their basic functionality is carried out. Examples of practical application of cloud services among well-known companies around the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 22, Issue 2

May 2022

582 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3490674

Editor:
Ling Liu
Georgia Institute of Technology, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 October 2021

Accepted: 01 October 2020

Revised: 01 September 2020

Received: 01 August 2020

Published in TOIT Volume 22, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Refereed

Funding Sources

Deanship of Scientific Research (DSR)

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
299
Total Downloads

Downloads (Last 12 months)47
Downloads (Last 6 weeks)2

Reflects downloads up to 03 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Clement NArce D(2024)Dynamics of Shared Security in the CloudInformation Systems Research10.1287/isre.2023.0256Online publication date: 29-Jul-2024
https://doi.org/10.1287/isre.2023.0256
An LQiu JZhang HLiu C(2024)Design of distributed network intrusion prevention system based on Spark and P2DR modelsCluster Computing10.1007/s10586-024-04487-327:8(10757-10776)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s10586-024-04487-3
ÇAY AKASAS DEREL-OZCEVİK M(2022)Attendance as a Service: A multi-layered system design in attendance applications for Covid-19Celal Bayar Üniversitesi Fen Bilimleri Dergisi10.18466/cbayarfbe.102824218:3(265-273)Online publication date: 29-Sep-2022
https://doi.org/10.18466/cbayarfbe.1028242

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents