The computer network security evaluation is a classical multiple attribute group decision making (MAGDM) problems. Recently, the Exponential TODIM TODIM (ExpTODIM) method has been used to cope with MAGDM issues. The probabilistic linguistic term sets (PLTSs) are used as a tool for characterizing uncertain information during the computer network security evaluation. In this manuscript, the probabilistic linguistic ExpTODIM (PL-ExpTODIM) is built to solve the MAGDM under PLTSs. In the end, a numerical case study for computer network security evaluation is given to validate the proposed method. The main contribution of this paper is constructed: (1) the Exponential TODIM TODIM (ExpTODIM) method is extended to the PLTSs; (2) the probabilistic linguistic ExpTODIM (PL-ExpTODIM) method is defined to solve the MAGDM under PLTSs; (3) In the end, a numerical case study for computer network security evaluation is given to validate the proposed method.
AbrahamASmithKJainRJainL. Network and information security: A computational intelligence approach Special Issue of Journal of Network and Computer Applications. J Netw Comput Appl.2007; 30(1): 1-3.
2.
AdamsG. Network security: It’s not just about computers anymore. Commun News.2000; 37(6): 32.
3.
AnagunAS. Development of committee neural network for computer access security system. In: Gavrilova M, Gervasi O, Kumar V, Tan CJK, Taniar D, Lagana A, et al., editors. Computational Science and Its Applications – Iccsa 2006, Pt 3. Lecture Notes in Computer Science. 3982. Berlin: Springer-Verlag Berlin; 2006. pp. 11-20.
EngJ. Computer network security for the radiology enterprise. Radiology.2001; 220(2): 303-9.
6.
WangYJMaJSharmaASinghPKGabaGSMasudM, et al. An exhaustive research on the application of intrusion detection technology in computer network security in sensor networks. J Sens.2021; 2021: 11.
7.
WuYLThuraisinghamBXiangY. Special issue on frontiers of trust, security and privacy for computer and communication networks. J Internet Technol.2014; 15(3): 451-2.
8.
XuB. Methods for evaluating the computer network security with fuzzy number intuitionistic fuzzy dual Hamy mean operators. J Intell Fuzzy Syst.2020; 39(3): 4427-41.
9.
ZhaoJWMasoodRSeneviratneS. A review of computer vision methods in network security. IEEE Commun Surv Tutor.2021; 23(3): 1838-78.
10.
ZhengZ. Analysis of application strategies of computer information management technology in maintaining network security. Basic Clin Pharmacol Toxicol.2019; 125: 225.
11.
ZouHLWuQL. Optimization strategy of computer network information security from the perspective of cognitive impairment. Psychiatr Danub.2022; 34: S286-S7.
12.
ZhangY. Research on the computer network security evaluation based on the DHFHCG operator with dual hesitant fuzzy information. J Intell Fuzzy Syst.2015; 28(1): 199-204.
13.
ZhangYPXiaoYChenMZhangJYDengHM. A survey of security visualization for computer network logs. Secur Commun Netw.2012; 5(4): 404-21.
14.
ZhaoHWangLX. An analysis of internet of things computer network security and remote control technology. Wirel Commun Mob Comput.2022; 2022: 13.
15.
SasakiKWatanabeNSakabeNSakabeK. Computer network system with security for a protein data collection system at the Photon Factory. J Synchrot Radiat.1999; 6: 116-8.
16.
SchechterSESmithMD. How much security is enough to stop a thief? The economics of outsider theft via computer systems and networks. In: Wright RN, editor. Financial Cryptography, Proceedings. Lecture Notes in Computer Science. 2742. Berlin: Springer-Verlag Berlin; 2003. pp. 122-37.
17.
ShandilyaSKUpadhyaySKumarANagarAK. AI-assisted Computer Network Operations testbed for Nature-Inspired Cyber Security based adaptive defense simulation and analysis. Futur Gener Comp Syst.2022; 127: 297-308.
18.
LiuWG. Computer network confidential information security based on big data clustering algorithm. Wirel Commun Mob Comput.2022; 2022: 10.
19.
MaJLiSB. The construction method of computer network security defense system based on multisource big data. Sci Program.2022; 2022: 13.
20.
MohammadiSSherkatMHJamporazmeyM. A taxonomy framework based on ITU-TX-805 security architecture for quantitative determination of computer network vulnerabilities. Secur Commun Netw.2013; 6(7): 864-80.
21.
KraemerSCarayonP. Human errors and violations in computer and information security: The viewpoint of network administrators and security specialists. Appl Ergon.2007; 38(2): 143-54.
22.
LiL. Application of data image encryption technology in computer network information security. Math Probl Eng.2022; 2022: 7.
23.
LinSYangMLuYChenLQ. The application of computer intelligence in the cyber-physical business system integration in network security. Comput Intell Neurosci.2022; 2022: 10.
24.
LinkovIVargheseAJamilSSeagerTPKikerGBridgesT, editors. Multi-criteria decision analysis: A framework for structuring remedial decisions at contaminated sites. NATO Advanced Research Workshop on Comparative Risk Assessment and Environmental Decision Making; 2002 Oct 13–16; Rome, ITALY. DORDRECHT: Springer; 2004.
25.
PusicDLusicZ. Multi-criteria decision analysis for nautical anchorage selection. Journal of Marine Science and Engineering.2023; 11(4): 21.
26.
SahaAVilluriVGKBhardwajAKumarS. A Multi-Criteria Decision Analysis (MCDA) Approach for Landslide Susceptibility Mapping of a Part of Darjeeling District in North-East Himalaya, India. Applied Sciences-Basel.2023; 13(8): 23.
27.
WieckowskiJWatrobskiJKizielewiczBSalabunW. Complex sensitivity analysis in Multi-Criteria Decision Analysis: An application to the selection of an electric car. Journal of Cleaner Production.2023; 390: 15.
28.
WilkinsCRychertMQueiroloRLentonSRKilmerBFischerB, et al. Assessing options for cannabis law reform: A Multi-Criteria Decision Analysis (MCDA) with stakeholders in New Zealand. Int J Drug Policy.2022; 105: 10.
29.
BhuyanMJDekaNSaikiaA. Micro-spatial flood risk assessment in Nagaon district, Assam (India) using GIS-based multi-criteria decision analysis (MCDA) and analytical hierarchy process (AHP). Risk Analysis. 2023; 16.
30.
IwehCDSemassouGCAhouansouRH. Optimum Site Selection of Hybrid Solar Photovoltaic (PV)-Hydro Power Plants in off Grid Locations in Cameroon using the Multi-Criteria Decision Analysis (MCDA). Energy Sources Part a-Recovery Utilization and Environmental Effects.2023; 45(3): 8076-91.
31.
MunierN. A new approach to the rank reversal phenomenon in MCDM with the SIMUS method. Multiple Criteria Decision Making.2016; (11): 137-52.
32.
StoilovaS. An integrated multi-criteria approach for planning railway passenger transport in the case of uncertainty. Symmetry [Internet].2020; 12(6).
33.
StoilovaSMunierNKendraMSkrúcanýT. Multi-Criteria Evaluation of Railway Network Performance in Countries of the TEN-T Orient-East Med Corridor. Sustainability.2020; 12(4): 1482.
34.
StoilovaSMunierN. A novel fuzzy SIMUS multicriteria decision-making method. An application in railway passenger transport planning. Symmetry [Internet]. 2021; 13(3): 483.
35.
WieckowskiJKizielewiczBShekhovtsovASalabunW. RANCOM: A novel approach to identifying criteria relevance based on inaccuracy expert judgments. Engineering Applications of Artificial Intelligence.2023; 122: 21.
36.
StanujkicDKarabasevicDZavadskasEKSmarandacheFBrauersWKM. A bipolar fuzzy extension of the MULTIMOORA method. Informatica.2019; 30(1): 135-52.
37.
RoyJSharmaHKKarSZavadskasEKSaparauskasJ. An extended COPRAS model for multi-criteria decision-making problems and its application in web-based hotel evaluation and selection. Economic Research-Ekonomska Istrazivanja.2019; 32(1): 219-53.
38.
LiuPDChenSM. Group decision making based on heronian aggregation operators of intuitionistic fuzzy numbers. Ieee Transactions on Cybernetics.2017; 47(9): 2514-30.
39.
YuGFFeiWLiDF. A compromise-typed variable weight decision method for hybrid multiattribute decision making. Ieee Transactions on Fuzzy Systems.2019; 27(5): 861-72.
TverskyAKahnemanD. Prospect theory: An analysis of decision under risk. Econometrica.1979; 47(2): 263-91.
42.
GomesLLimaM. TODIM: Basics and application to multicriteria ranking of projects with environmental impacts. Foundations of Computing and Decision Sciences.1979; 16: 113-27.
43.
LeonetiABGomesL. A novel version of the TODIM method based on the exponential model of prospect theory: The ExpTODIM method. European Journal of Operational Research.2021; 295(3): 1042-55.
44.
SunHYangZCaiQWeiGWMoZW. An extended Exp-TODIM method for multiple attribute decision making based on the Z-Wasserstein distance. Expert Systems with Applications.2023; 214: 14.
45.
PangQWangHXuZS. Probabilistic linguistic linguistic term sets in multi-attribute group decision making. Information Sciences.2016; 369: 128-43.
46.
LM.W.,XZ.S.,Probabilistic Linguistic Distance Measures and Their Applications in Multi-criteria Group Decision Making, In: Collan M, Kacprzyk J (eds) Soft Computing Applications for Group Decision-making and Consensus Modeling Studies in Fuzziness and Soft Computing Springer, Cham. 2018; 357.
47.
ShannonCE. A mathematical theory of communication. Bell System Technical Journal.1948; 27(4): 379-423.
48.
DarkoAPLiangDC. Probabilistic linguistic WASPAS method for patients’ prioritization by developing prioritized Maclaurin symmetric mean aggregation operators. Applied Intelligence.2022; 52(8): 9537-55.
49.
PanLRenPJXuZS. Therapeutic Schedule Evaluation for Brain-Metastasized Non-Small Cell Lung Cancer with A Probabilistic Linguistic ELECTRE II Method. International Journal of Environmental Research and Public Health.2018; 15(9): 23.
50.
MaYFZhaoYYWangXYFengCYZhouXYLevB. Integrated BWM-Entropy weighting and MULTIMOORA method with probabilistic linguistic information for the evaluation of Waste Recycling Apps. Applied Intelligence.2023; 53(1): 813-36.
51.
ChenLXGouXL. The application of probabilistic linguistic CODAS method based on new score function in multi-criteria decision-making. Computational & Applied Mathematics.2022; 41(1): 25.
52.
LiHLLiuSLWangWZ. The probabilistic linguistic term sets based ORESTE method for risk evaluation in Fine-Kinney model with interactive risk factors. Journal of Intelligent & Fuzzy Systems.2022; 43(3): 3493-512.
53.
SalabunWUrbaniakK, editors. A New Coefficient of Rankings Similarity in Decision-Making Problems. 20th Annual International Conference on Computational Science (ICCS); 2020 Jun 03–05; Amsterdam, NETHERLANDS. CHAM: Springer International Publishing Ag; 2020.
54.
SałabunWWa̧tróbskiJShekhovtsovA. Are MCDA Methods Benchmarkable? A Comparative Study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II Methods. Symmetry.2020; 12(9): 1549.
55.
LinMWXuZSZhaiYLYaoZQ. Multi-attribute group decision-making under probabilistic uncertain linguistic environment. Journal of the Operational Research Society.2018; 69(2): 157-70.
56.
GongKXChenCF. A programming-based algorithm for probabilistic uncertain linguistic intuitionistic fuzzy group decision-making. Symmetry-Basel.2019; 11(2).
57.
XieWYRenZLXuZSWangH. The consensus of probabilistic uncertain linguistic preference relations and the application on the virtual reality industry. Knowledge-Based Systems.2018; 162: 14-28.
58.
WeiGWHeYLeiFWuJWeiCGuoYF. Green supplier selection with an uncertain probabilistic linguistic MABAC method. Journal of Intelligent & Fuzzy Systems.2020; 39(3): 3125-36.
59.
ZhaoMWGaoHWeiGWWeiCGuoYF. Model for network security service provider selection with probabilistic uncertain linguistic TODIM method based on prospect theory. Technological and Economic Development of Economy.2022; 28(3): 638-54.
60.
YuWWZhangHLiBQ. Comparison and operators based on uncertain probabilistic linguistic term set. Journal of Intelligent & Fuzzy Systems.2019; 36(6): 6359-79.
61.
SuYZhaoMWWeiGWWeiCChenXD. Probabilistic Uncertain Linguistic EDAS Method Based on Prospect Theory for Multiple Attribute Group Decision-Making and Its Application to Green Finance. International Journal of Fuzzy Systems.2022; 24(3): 1318-31.
62.
JinCWangHXuZS. Uncertain probabilistic linguistic term sets in group decision making. International Journal of Fuzzy Systems.2019; 21(4): 1241-58.
