Measuring the Information Security Maturity of Enterprises under Uncertainty Using Fuzzy AHP
Author(s) -
Adel A. Nasser,
Abdualmajed A. Al-Khulaidi,
Mijahed N. Aljober
Publication year - 2018
Publication title -
international journal of information technology and computer science
Language(s) - English
Resource type - Journals
eISSN - 2074-9015
pISSN - 2074-9007
DOI - 10.5815/ijitcs.2018.04.02
Subject(s) - analytic hierarchy process , computer science , vagueness , maturity (psychological) , multiple criteria decision analysis , information security , fuzzy logic , process (computing) , capability maturity model , fuzzy set , common criteria , operations research , data mining , risk analysis (engineering) , management science , computer security , artificial intelligence , mathematics , business , psychology , developmental psychology , software , programming language , economics , operating system
Generally, measuring the Information Security maturity(ISM) is the first step to build a new knowledge information security management system in an organization. Knowing the ISM level helps organizations decide the type of protection strategies and policies will be taken and their priorities to strengthen their competitive ability. One of the possible ways to solve the problem is a using multiple criteria decision-making (MCDM) methodology. Analytic hierarchy process (AHP) is one of the most commonly used MCDM methods, which combines subjective and personal preferences in the information security assessment process. However, the AHP involves human subjectivity, which introduces vagueness type of uncertainty and requires the use of decision-making under those uncertainties. In this paper, the IS maturity is based on hierarchical multilevel information security gap analysis model for ISO 27001:2013 security standard. The concept of fuzzy set is applied to Analytic Hierarchical Process (AHP) to propose a model for measuring organizations IS maturity under uncertain environment. Using fuzzy AHP approach helps determine more efficiently importance weights of factors and indicators, especially deal with imprecise and uncertain expert comparison judgments. A case study is used to illustrate the better new method for IS evaluation.
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