Using Fuzzy Logic to Increase the Accuracy of E-Commerce Risk Assessment Based on an Expert System

H. Beheshti, M. Alborzi

Abstract


Strong adaptive control can be exercised even without access to accurate data inputs. Such control is possible through fuzzy mathematics, which is a meta-collection of Boolean logic principles that imply relative accuracy. Fuzzy mathematics find applications in e-commerce, where different risk analysis methods are available for risk assessment and estimation. Such approaches can be quantitative or qualitative, depending on the type of examined data. Quantitative methods are grounded in statistics, whereas qualitative methods are based on expert judgments and fuzzy set theory. Given that qualitative methods are very subjective and deal with vague or inaccurate data, fuzzy logic can be used to extract useful information from data inaccuracies. In this study, a model based on the opinions of e-commerce security experts was designed and implemented by using fuzzy expert systems and MATLAB. A case study was conducted to validate the effectiveness of the Model.


Keywords


fuzzy logic; risk assessment system; e-commerce; expert system

Full Text:

PDF

References


ISO/IEC, ISO/IEC Guide 73:2003: Risk management - Principles and guidelines, International Organization for Standardization, Switzerland, 2003

M. R. Gupta, S. Sarkar, S. Ghosh, M. Debnath, M. Khan, “Effect of nonadiabaticity of dust charge variation on dust acoustic waves: Generation of dust acoustic shock waves”, Physical Review E, Vol. 63, No. 4, pp.046406, 2001

W. Jiang, Z. Li, J. Jia, D. Liu, “Evaluating E-Commerce System Security Using Fuzzy Multi-criterion Decision-Making”, IEEE Seventh International Conference on Semantic Computing, pp. 438-443, 2013

E. W. T. Ngai, F. K. T. Wat, “Fuzzy decision support system for risk analysis in e-commerce development” Decision support systems, Vol. 40, No. 2, pp.235-255, 2005

K. Darlington, The essence of expert systems, Prentice Hall, 2000

A. S. Sodiya, H. O. D. Longe, O. M. Fasan, “Software security risk analysis using fuzzy expert system”, INFOCOMP Journal of Computer Science, Vol. 7, No. 3, pp.70-77, 2008

M. H. Zirakja, R. Samizadeh, “Risk Analysis in E-commerce via Fuzzy Logic”, International Journal of Management and Business Research, Vol. 1, No. 3, pp.99-112, 2011

A. S. Sendi, M. Jabbarifar, M. Shajari, M. Dagenais, “FEMRA: Fuzzy Expert Model for Risk Assessment”, Fifth International Conference on Internet Monitoring and Protection, pp. 48-53, 2010

W. L. McGill, B. M. Ayyub, “Multicriteria security system performance assessment using fuzzy logic”, The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, Vol. 4, No. 4, pp.356-376, 2007

K. Shang, Inconsistent Inference in Qualitative Risk Assessment, Available at http://crocouncil.org/images/Inconsistent_Inference_in

_Qualitative_Risk_Assessment_v2_-_clean.pdf, 2013

A. Ozdagoglu, G. Ozdagoglu, “Comparison of AHP and Fuzzy AHP for the Multi Criteria Decision Making Processes with Linguistic Evaluations”, Istanbul Ticaret Universitesi Fen Bilimleri Dergisi, Vol. 6, No.l1, pp. 65-85, 2007

D. Y. Chang, “Applications of The Extent Analysis Method on Fuzzy- AHP”, European Journal of Operational Research, Vol. 95, No. 3,pp. 649-655, 1996

K. Mostafa, Fuzzy logic in MATLAB, Tehran: Kian university press, 2011

N. Kasuan, N. Ismail, M. N. Taib, M. H. Fazalul Rahiman, “Recurrent adaptive neuro-fuzzy inference system for steam temperature estimation in distillation of essential oil extraction process”, IEEE 7th International Colloquium on Signal Processing and its Applications, pp. 1-6, 2011




eISSN: 1792-8036     pISSN: 2241-4487