480 494
Full Length Article
International Journal of Neutrosophic Science
Volume 20 , Issue 2, PP: 55-76 , 2023 | Cite this article as | XML | Html |PDF

Title

An integrated AHP MCDM based Type-2 Neutrosophic Model for Assessing the Effect of Security in Fog-based IoT Framework

  Mohammad D. Alshehri 1 *

1  Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
    (Alshehri@tu.edu.sa)


Doi   :   https://doi.org/10.54216/IJNS.200205

Received: June 11, 2022 Accepted: January 16, 2023

Abstract :

The term "Internet of Things" (IoT) refers to a network of connected, intelligent devices that are responsible for the collecting and dissemination of data. Because technology automates the tasks we do daily, our lives have become simpler as a result. However, with a typical architecture for the cloud and the Internet of Things, real-time data processing is not always practicable. This is particularly true for latency-sensitive apps. This eventually resulted in the development of fog computing. On the one hand, the fog layer may perform computations and data processing at the very edge of the network, which enables it to provide results more quickly. On the other hand, this pushes the attack surface closer to the machines themselves, which is a security risk. Because of this, the sensitive data that is stored on the layer is now susceptible to assaults. Therefore, considering the security of the fog-IoT is of the utmost significance. A system or platform's level of security is determined by a number of different elements. When it comes to conducting an accurate risk assessment, the sequence in which these considerations are considered is of the utmost importance. Because of this, determining the level of security offered by fog and IoT devices becomes a Multi-Criteria Decision-Making (MCDM) dilemma. This article presents a two-stage hybrid multi-criteria decision-making model that is based on type-2 neutrosophic numbers (T2NNs). The goal of this article is to give scientists and practitioners a decision-making tool that is both easy and versatile. The initial step of this process is determining the weights of criteria by the AHP method in the T2NN environment. Second, the T2NN-based Multi-Attributive Border Approximation area Comparison (MABAC) method is used to rank the various fog security based on IoT. Both of these methods are described in more detail below. With the help of the comparison study, the high reliability and robustness of the combined AHP and MABAC based type-2 neutrosophic model have been proven.

Keywords :

Type-2 Neutrosophic; AHP; IoT and Security.

References :

 

[1]         N. Tariq et al., “The security of big data in fog-enabled IoT applications including blockchain: A survey,” Sensors, vol. 19, no. 8, p. 1788, 2019.

[2]         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 Fog computing: Breakthroughs in research and practice, IGI global, 2018, pp. 365–378.

[3]         K. Lee, D. Kim, D. Ha, U. Rajput, and H. Oh, “On security and privacy issues of fog computing supported Internet of Things environment,” in 2015 6th International Conference on the Network of the Future (NOF), 2015, pp. 1–3.

[4]         J. Ni, K. Zhang, X. Lin, and X. Shen, “Securing fog computing for internet of things applications: Challenges and solutions,” IEEE Communications Surveys & Tutorials, vol. 20, no. 1, pp. 601–628, 2017.

[5]         O. Salman, I. Elhajj, A. Chehab, and A. Kayssi, “IoT survey: An SDN and fog computing perspective,” Computer Networks, vol. 143, pp. 221–246, 2018.

[6]         A. Alrawais, A. Alhothaily, C. Hu, and X. Cheng, “Fog computing for the internet of things: Security and privacy issues,” IEEE Internet Computing, vol. 21, no. 2, pp. 34–42, 2017.

[7]         M. D. Alshehri and F. K. Hussain, “A fuzzy security protocol for trust management in the internet of things (Fuzzy-IoT),” Computing, vol. 101, no. 7, pp. 791–818, 2019.

[8]         K. Tange, M. De Donno, X. Fafoutis, and N. Dragoni, “A systematic survey of industrial Internet of Things security: Requirements and fog computing opportunities,” IEEE Communications Surveys & Tutorials, vol. 22, no. 4, pp. 2489–2520, 2020.

[9]         P. Karthika, R. Ganesh Babu, and P. A. Karthik, “Fog computing using interoperability and IoT security issues in health care,” in Micro-Electronics and Telecommunication Engineering: Proceedings of 3rd ICMETE 2019, 2020, pp. 97–105.

[10]       D. Puthal, S. P. Mohanty, S. A. Bhavake, G. Morgan, and R. Ranjan, “Fog computing security challenges and future directions [energy and security],” IEEE Consumer Electronics Magazine, vol. 8, no. 3, pp. 92–96, 2019.

[11]       H. F. Atlam, R. J. Walters, and G. B. Wills, “Fog computing and the internet of things: A review,” big data and cognitive computing, vol. 2, no. 2, p. 10, 2018.

[12]       S. Javanmardi, M. Shojafar, R. Mohammadi, A. Nazari, V. Persico, and A. Pescapè, “FUPE: A security driven task scheduling approach for SDN-based IoT–Fog networks,” Journal of information security and applications, vol. 60, p. 102853, 2021.

[13]       M. Chiang and T. Zhang, “Fog and IoT: An overview of research opportunities,” IEEE Internet of things journal, vol. 3, no. 6, pp. 854–864, 2016.

[14]       M. D. Alshehri and F. K. Hussain, “A centralized trust management mechanism for the internet of things (CTM-IoT)," in Advances on Broad-Band Wireless Computing, Communication and Applications: Proceedings of the 12th International Conference on Broad-Band Wireless Computing, Communication and Applications (BWCCA-2017), 2018, pp. 533–543.

[15]       A. A. Mutlag, M. K. Abd Ghani, N. al Arunkumar, M. A. Mohammed, and O. Mohd, “Enabling technologies for fog computing in healthcare IoT systems,” Future Generation Computer Systems, vol. 90, pp. 62–78, 2019.

[16]       A. Rauf, R. A. Shaikh, and A. Shah, “Security and privacy for IoT and fog computing paradigm,” in 2018 15th Learning and Technology Conference (L&T), 2018, pp. 96–101.

[17]       N. Abbas, M. Asim, N. Tariq, T. Baker, and S. Abbas, “A mechanism for securing IoT-enabled applications at the fog layer,” Journal of Sensor and Actuator Networks, vol. 8, no. 1, p. 16, 2019.

[18]       B. Mukherjee, R. L. Neupane, and P. Calyam, “End-to-end IoT security middleware for cloud-fog communication,” in 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud), 2017, pp. 151–156.

[19]       K. Dubey, S. C. Sharma, and M. Kumar, “A secure iot applications allocation framework for integrated fog-cloud environment,” Journal of Grid Computing, vol. 20, no. 1, p. 5, 2022.

[20]       H. Alyami et al., “Effectiveness evaluation of different IDSs using integrated fuzzy MCDM model,” Electronics, vol. 11, no. 6, p. 859, 2022.

[21]       M. D. Alshehri, F. K. Hussain, and O. K. Hussain, “Clustering-driven intelligent trust management methodology for the internet of things (CITM-IoT),” Mobile networks and applications, vol. 23, no. 3, pp. 419–431, 2018.,

[22]       M. Pouyakian, A. Khatabakhsh, M. Yazdi, and E. Zarei, “Optimizing the Allocation of Risk Control Measures Using Fuzzy MCDM Approach: Review and Application,” Linguistic Methods Under Fuzzy Information in System Safety and Reliability Analysis, pp. 53–89, 2022.

[23]       M. D. Alshehri and F. K. Hussain, “A comparative analysis of scalable and context-aware trust management approaches for internet of things,” in Neural Information Processing: 22nd International Conference, ICONIP 2015, November 9-12, 2015, Proceedings, Part IV 22, 2015, pp. 596–605.

[24]       M. Abdel-Basset, M. Mohamed, and A. K. Sangaiah, “Neutrosophic AHP-Delphi Group decision making model based on trapezoidal neutrosophic numbers,” Journal of Ambient Intelligence and Humanized Computing, vol. 9, no. 5, pp. 1427–1443, 2018.

[25]       E. Bolturk and C. Kahraman, “A novel interval-valued neutrosophic AHP with cosine similarity measure,” Soft Computing, vol. 22, no. 15, pp. 4941–4958, 2018.

[26]       M. Abdel-Basset, M. Mohamed, and F. Smarandache, “An extension of neutrosophic AHP–SWOT analysis for strategic planning and decision-making,” Symmetry, vol. 10, no. 4, p. 116, 2018.

[27]       M. Abdel-Basset, M. Mohamed, Y. Zhou, and I. Hezam, “Multi-criteria group decision making based on neutrosophic analytic hierarchy process,” Journal of Intelligent & Fuzzy Systems, vol. 33, no. 6, pp. 4055–4066, 2017.

[28]       M. A. Basset, M. Mohamed, A. K. Sangaiah, and V. Jain, “An integrated neutrosophic AHP and SWOT method for strategic planning methodology selection,” Benchmarking: An International Journal, vol. 25, no. 7, pp. 2546–2564, 2018.

[29]       P. Liu and S. Cheng, “An improved MABAC group decision-making method using regret theory and likelihood in probability multi-valued neutrosophic sets,” International Journal of Information Technology & Decision Making, vol. 19, no. 05, pp. 1353–1387, 2020.

[30]       D. Pamucar, M. Yazdani, R. Obradovic, A. Kumar, and M. Torres‐Jiménez, “A novel fuzzy hybrid neutrosophic decision‐making approach for the resilient supplier selection problem,” International Journal of Intelligent Systems, vol. 35, no. 12, pp. 1934–1986, 2020.

[31]       X. Peng and J. Dai, “Approaches to single-valued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function,” Neural Computing and Applications, vol. 29, no. 10, pp. 939–954, 2018.

[32]       P. Ji, H. Zhang, and J. Wang, “Selecting an outsourcing provider based on the combined MABAC–ELECTRE method using single-valued neutrosophic linguistic sets,” Computers & Industrial Engineering, vol. 120, pp. 429–441, 2018.

[33]       N. Rahim, L. Abdullah, and B. Yusoff, “A border approximation area approach considering bipolar neutrosophic linguistic variable for sustainable energy selection,” Sustainability, vol. 12, no. 10, p. 3971, 2020.

[34]       R. Şahin and F. Altun, “Decision making with MABAC method under probabilistic single-valued neutrosophic hesitant fuzzy environment,” Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 10, pp. 4195–4212, 2020.

[35]       I. Irvanizam, N. N. Zi, R. Zuhra, A. Amrusi, and H. Sofyan, “An extended MABAC method based on triangular fuzzy neutrosophic numbers for multiple-criteria group decision making problems,” Axioms, vol. 9, no. 3, p. 104, 2020.

[36]       R. Verma and S. Chandra, “Interval-valued intuitionistic fuzzy-analytic hierarchy process for evaluating the impact of security attributes in fog based internet of things paradigm,” Computer Communications, vol. 175, pp. 35–46, 2021.

[37]       M. Abdel-Basset, M. Saleh, A. Gamal, and F. Smarandache, “An approach of TOPSIS technique for developing supplier selection with group decision making under type-2 neutrosophic number,” Applied Soft Computing, vol. 77, pp. 438–452, 2019.

[38]       V. Simic, I. Gokasar, M. Deveci, and A. Karakurt, “An integrated CRITIC and MABAC based type-2 neutrosophic model for public transportation pricing system selection,” Socio-Economic Planning Sciences, vol. 80, p. 101157, 2022.


Cite this Article as :
Style #
MLA Mohammad D. Alshehri. "An integrated AHP MCDM based Type-2 Neutrosophic Model for Assessing the Effect of Security in Fog-based IoT Framework." International Journal of Neutrosophic Science, Vol. 20, No. 2, 2023 ,PP. 55-76 (Doi   :  https://doi.org/10.54216/IJNS.200205)
APA Mohammad D. Alshehri. (2023). An integrated AHP MCDM based Type-2 Neutrosophic Model for Assessing the Effect of Security in Fog-based IoT Framework. Journal of International Journal of Neutrosophic Science, 20 ( 2 ), 55-76 (Doi   :  https://doi.org/10.54216/IJNS.200205)
Chicago Mohammad D. Alshehri. "An integrated AHP MCDM based Type-2 Neutrosophic Model for Assessing the Effect of Security in Fog-based IoT Framework." Journal of International Journal of Neutrosophic Science, 20 no. 2 (2023): 55-76 (Doi   :  https://doi.org/10.54216/IJNS.200205)
Harvard Mohammad D. Alshehri. (2023). An integrated AHP MCDM based Type-2 Neutrosophic Model for Assessing the Effect of Security in Fog-based IoT Framework. Journal of International Journal of Neutrosophic Science, 20 ( 2 ), 55-76 (Doi   :  https://doi.org/10.54216/IJNS.200205)
Vancouver Mohammad D. Alshehri. An integrated AHP MCDM based Type-2 Neutrosophic Model for Assessing the Effect of Security in Fog-based IoT Framework. Journal of International Journal of Neutrosophic Science, (2023); 20 ( 2 ): 55-76 (Doi   :  https://doi.org/10.54216/IJNS.200205)
IEEE Mohammad D. Alshehri, An integrated AHP MCDM based Type-2 Neutrosophic Model for Assessing the Effect of Security in Fog-based IoT Framework, Journal of International Journal of Neutrosophic Science, Vol. 20 , No. 2 , (2023) : 55-76 (Doi   :  https://doi.org/10.54216/IJNS.200205)