A fuzzy inference system (FIS) to evaluate the security ......RESEARCH Open Access A fuzzy inference...

RESEARCH Open Access

A fuzzy inference system (FIS) to evaluatethe security readiness of cloud serviceprovidersSyed Rizvi1* , John Mitchell1, Abdul Razaque2, Mohammad R. Rizvi3 and Iyonna Williams1

Abstract

Cloud computing is a model for on-demand delivery of IT resources (e.g., servers, storage, databases, etc.) over theInternet with pay-as-you-go pricing. Although it provides numerous benefits to cloud service users (CSUs) such asflexibility, elasticity, scalability, and economies of scale, there is a large trust deficit between CSUs and cloud serviceproviders (CSPs) that prevents the widespread adoption of this computing paradigm. While some businesses haveslowly started adopting cloud computing with careful considerations, others are still reluctant to migrate toward itdue to several data security and privacy issues. Therefore, the creation of a trust model that can evolve to reflectthe true assessment of CSPs in terms of either a positive or a negative reputation as well as quantify trust level is ofutmost importance to establish trust between CSUs and CSPs. In this paper, we propose a fuzzy-logic basedapproach that allows the CSUs to determine the most trustworthy CSPs. Specifically, we develop inference rulesthat will be applied in the fuzzy inference system (FIS) to provide a quantitative security index to the CSUs. One ofthe main advantages of the FIS is that it considers the uncertainties and ambiguities associated with measuringtrust. Moreover, our proposed fuzzy-logic based trust model is not limited to the CSUs as it can be used by theCSPs to promote their services through self-evaluation. To demonstrate the effectiveness of our proposed fuzzy-based trust model, we present case studies where several CSPs are evaluated and ranked based on the securityindex.

Keywords: Cloud computing, Trust model, Cloud service user, Cloud service provider, Fuzzy-logic

IntroductionCloud computing is a distributed computing environ-ment capable of storing large quantities of data, increas-ing data processing efficiency, and scaling an applicationbased on demand [1]. It utilizes virtualization toprovision resources and offers web-based services at afixed cost. Collectively known as the SPI model, thethree main services that this model offers are: Softwareas a Service (SaaS), Platform as a Service (PaaS), and In-frastructure as a Service (IaaS). Google Docs, GoogleApp Engine, and Amazon Elastic Compute Cloud (EC2)

are examples of SaaS, PaaS, and IaaS, respectively. Fur-thermore, the cloud supports a variety of deploymentmodels such as private, community, public, or hybridmodels. Some of the benefits of employing a cloud ser-vice or deployment method include flexibility, scalability,pay-per-use, and accessibility through a web browser [2].In addition, the amalgamation of IT resources, datawarehousing, computation outsourcing, and multi-tenancy are several important concepts that increasecloud efficiency. On the other hand, these features alsoprovide several opportunities for attackers to performmalicious activities such as compromising the integrityof the outsourced data or unauthorized access to cloudservices, etc. Thus, the fundamental concepts present achallenge in the security and privacy domain that must

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* Correspondence: [email protected] of Information Sciences and Technology, Pennsylvania StateUniversity, Altoona, PA 16601, USAFull list of author information is available at the end of the article

Journal of Cloud Computing:Advances, Systems and Applications

Rizvi et al. Journal of Cloud Computing: Advances, Systems and Applications (2020) 9:42 https://doi.org/10.1186/s13677-020-00192-9

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be resolved in order to establish trust among the cloudstakeholders (e.g., CSUs, CSPs, third parties, etc.).In the present scenario, trust plays a key role in influ-

encing the decision to utilize cloud services for enter-prises. Trust is based on one entity’s own experienceswithin a specific amount of time and involves the beliefin a second entity’s capabilities, reliability, and honesty[3]. Trust can be dissected into two types: subjective andobjective. Subjective trust focuses on the user’s percep-tion of a reputation that develops from utilizing a ser-vice. Subjective trust is pertinent as it emphasizes thegradual formulation of trust based upon continuousinteraction and encompasses one of the main trust com-ponents which is the user’s perception of the intentionsof the other party [4–6]. The subjective trust used in thispaper combines direct, recommendation, and reputationtrust in order to create a comprehensive trust but with adegree of uncertainty [3]. The second type is an objectivetrust that involves quantitative data. Objective trust onlytakes into account the data, graphs, and equations in-volved to assess the mathematical trust value. Objectivetrust portrays a black and white numerical value of trustrather than considering the grey areas that arise whendealing with cloud service users choosing a CSP. Since theinput of the CSUs are the source of our data sets, the useof subjective trust can be well justified with our fuzzy-logic based trust model. The operationalization of subject-ive trust is found in quantifying the overall trustworthinessof a CSP based on the input of cloud users.Trustworthiness of a CSP and the willingness of the

CSU are two important factors to analyze when develop-ing a trust model. Factors that influence trust when deal-ing with technology vary as there are influences thatdeal with systemized information about a particular de-vice or service, and aesthetics or design that appeals tothe user in order to formulate persuasion of gained usertrust. While the popular constituents of subjective trustencompass its quantitative approach, there are qualita-tive characteristics that heavily influence subjective trustsuch as aesthetics. Gaining a user’s trust not only focuseson rationality but if the user enjoys the product, its de-sign, and the ease of operation [7]. Researchers have pre-viously found that understanding how emotions relate totrust is a key component in gaining full trust of a user.Allowing a user to be emotionally expressive will inclinethem to increase their trustworthiness of a service [8].Our use of fuzzy logic addresses the emotional aspect ofsubjective trust as it analyzes the uncertainties that mayarise when users assess the reliability of a CSP as well astheir inclination to trust a certain service because of aes-thetics. Trust values can be obtained from our proposedmodel that will allow for a comparison between theCSPs. To calculate trust, factors that influence the trust-worthiness of a CSP must be identified [9].

As stated earlier, trust cannot be established until thesecurity concerns associated with cloud computing areevaluated. The security of a CSP is one of the most sig-nificant attributes that should be analyzed in order todetermine the trust value. The security issues generatingthe most concerns include data breaches or losses. Ingeneral, the main problem is that most of the CSUspresently do not feel confident in outsourcing their sen-sitive information into the cloud due to security andprivacy concerns [10]. Specifically, the most pertinent se-curity issues are; lack of security standards, lack ofservice-level agreements (SLAs), vulnerabilities with theinternet protocol, malicious insiders, account hijacking,insecure API interface, multi-tenancy, and data privacy,to name a few. For instance, the abstraction of SLAsdamages the reliability of CSPs since it does not provideguarantees in regard to ownership of data, availability ofservices, transparency in the deployed security controls,etc. against many different types of attacks. For example,Internet vulnerability protocols are network-based andmay allow for distributed denial-of-service (DDoS) at-tacks [11]. Similarly, malicious insiders could erase userdata and potentially sell sensitive information to rival or-ganizations. Furthermore, the virtualization aspect of thecloud facilitates the most serious attacks since virtualmachines are utilized by attackers [12].To address these concerns, several frameworks and

models have recently proposed to evaluate CSPs and es-tablish trust between customers and service providersthat can evolve. In general, the existing trust models[13–15] for cloud computing are not fully capable ofproviding an accurate method for measuring trust, espe-cially in the presence of incomplete sets of input data orobservations. The CSPs differ with each other in termsof their security strengths, which present a challenge forresearchers trying to develop an efficient and accuratemodel for security evaluation. Several limitations mustbe overcome to develop a strong trust model that canaccurately reflect a true comparison of competing CSPsusing partial sets of input data or observations. For ex-ample, some trust models only measure the indirecttrust based on the user’s recommendations. The disre-gard for direct evidence results in the lack of suitableevaluation factors or a dependence on recommendationsto quantify results [3]. Another limitation is the fixedweight assigned to each factor [13]. The expert know-ledge used for weight allocation does not allow foradaptability in a practical environment. We believe thatthe preferences of the CSUs should be taken into ac-count in determining the importance and weight of thefactors. Currently, many trust models [13–15] used forevaluating CSPs or similar distributed systems aredependent on certain values or subjective logic. How-ever, there is a general vagueness that surrounds the

Rizvi et al. Journal of Cloud Computing: Advances, Systems and Applications (2020) 9:42 Page 2 of 17

concept of trust which promotes uncertainties duringthe evaluation process. For instance, one CSU couldconsider a CSP to be trustworthy, while the same CSPappears entirely untrustworthy to another CSU. Subject-ive logic is adopted in other trust models to accept un-certain values and increase the accuracy of the finalresults. On the other hand, subjective logic fails toaccept values that are represented as vague statements.Furthermore, linguistic statements enable the CSU to in-put data more accurately since linguistics are easy forhumans to understand [10].Motivated by this, we develop a trust model that can as-

sist a CSU in comparing the potential CSPs using theirown security preferences. This research work is the con-tinuation of our initial findings published in [16]. To fur-ther extend our trust model, we implement fuzzy logic toprocess the subjectivity of the CSUs and obtain the mostaccurate results. Fuzzy logic uses values that are not re-stricted to a binary decision of yes or no [2]. Instead, theattribute being evaluated is assigned a membership thatranges from low to high. Therefore, fuzzy logic can acceptimpartial data sets, which is a high possibility when evalu-ating trustworthiness due to ambiguity and uncertaintybeing common in these circumstances. There are threestages of fuzzy logic which include Fuzzification, inferenceengine, and defuzzification [17]. Fuzzification involves theCSU inputting the data which is then matched with thefactors used for evaluation and then converted into a fuzzydata set. The inference engine contains nonlinear mappingand the mapping rules that determine the output. Finally,the output (Security Index) is approximated after defuzzi-fication when the data sets are turned into crisp data. Tosupport the three stages of fuzzy-logic, we identify theevaluation factors using linguistic variables which makeour trust-model user friendly. In addition, we developmapping rules for the inference engine to process the in-put data set.Research Contributions: The following are some of the

contributions that make our research unique fromothers existing work:

1. Use of Fuzzy logic to evaluate CSPs with a newtrust model: Our first main contribution is utilizingthe process of fuzzification and defuzzification onthe set of input data in order to evaluate serviceproviders. The set of input data is often incompletewith ambiguity and uncertainty – making it difficultfor to apply conventional methods of evaluation.Our contribution differs from other research as weuse the fuzzy logic process for CSP securityassessment and to establish trust between a CSUand CSP even in the presence of impartial data sets.

2. Developing experimental case studies: Our secondmain contribution is developing case studies based

on hypothetical scenario to exemplify how ourproposed trust model can be used in real worldsituations. Our first case study analyzes the event ofa CSU evaluating their current CSP to determine itssecurity strength and weaknesses. Our second casestudy shows how a CSP can use our proposedmodel to self-evaluate. Our last case study exempli-fies the concept of a CSU trying to find the bestCSP for security and using our model for a bettersecurity assessment.

3. Trust level validation of a CSP: Our third maincontribution is the addition of mathematical modelto evaluate the trust of a CSP. Using our fuzzy-logictrust model, we can use the equations to not onlyperform a security assessment, but also audit CSPsto determine their trust level.

Related workIn the context of cloud computing, there are severaltrust models recently proposed using a variety ofmethods to maintain the confidence of clients. Cur-rently, most of the trust models proposed in literatureare dependent on the assumptions or inefficient parame-ters which limit the practicality. This section highlights afew notable works, closely related to ours, whose authorshave presented significant contributions in the trustmanagement domain.Rathi and Kolekar [18] specifically narrow the various

parameters involved in trusting a CSP by separating thecomponents that are most valuable when choosing afaithful service. The generated trust model that is imple-mented focuses on safety of data, individuality manage-ment, authorization, authentication, and virtualization toprovide maximum efficient security involving a CSP.The proposed trust model analyzes security parametersevaluated by a cloud consumer as the input value andthe trust value is calculated by a point-based system thattakes the ratio of user gained points to total points pos-sible. This allows the user to choose their cloud servicethat can cater to their specified needs. The problem withthis approach is that the cloud user may not necessarilychoose the CSP that has maximum benefits in each par-ameter; what may be trusted in a specific area may notbe as effective in others. Ragavendiran et al. [10] proposea model to develop a trust score of a CSP by comparingService Broker and Load Balancing policies and usingthe fuzzy inference system. In this approach, three Ser-vice Broker Policies and three Load Balancing Policiesare taken into account when calculating the trust scoreof a CSP. The architecture of the trust model includesUser Bundles and Data Centers of the service providersto individually compare the services for optimal selectionfor the user. The Service Broker and Load Balancing pol-icies are used to consider the Data Center sectors such


as IP, network, storage and applications. The policiesthen operate on user input such as their region, whichthen calculates the number of Data Centers in that par-ticular region, and finally sends the request to that spe-cific Data Center. Fuzzy inference allows aggregationand defuzzification to then determine which factors ofthe CSPs from the Data Centers are directly propor-tional to determine the overall trust score. The drawbackto this approach is that cloud service configuration var-ies, and the trust model assumes that all configurationsacross the regions are alike. Farrokhi and Nalbandian[19] propose a trust using fuzzy logic similar to [10],however, the main focus is on the management of trust.The trust assessment is based on an equation that calcu-lates the difference of direct and indirect trust alongwith trust weight and interaction weight. The downsideto using this method is that calculating trust in a fuzzyenvironment involves cloud network types, trust man-agement types, and trust scores; that is not addressed bythe authors.Mohammed et al. [9] proposed a trust model similar

to [18] where the trust percentage is calculated for theconsumer based on the trust metric stage. However, theparameters include reputation, customer experience,majority consensus, and cloud service consumer’s cap-ability which differs from [18]. The trust metric stage iswhere the trust percentage for each consumer is calcu-lated and uses techniques such as Particle SwarmOptimization, Multiple Regression, Analytic HierarchicalProcess, and Particle Swarm Optimization-Multiple Re-gression. However, although these techniques seem tobe accurate, there still is a percentage of average devi-ation that would only be fixed by developing a schedul-ing process for the consumers to test the model basedon their trust values. Wang et al. [3] proposed a trustmodel that encompasses both weights and gray correl-ation analysis. The generalized trust is converted intosubsections that include recommendation trust, compre-hensive trust, and overall trust. Using the rough set the-ory and analytic hierarchy process-based method, directtrust is calculated which then can provide an accurateoverall trust. A flaw in this proposed trust model is thatthe satisfaction factor that is implemented with recom-mendation trust. Even if the reputation seems to begood, there is not a guarantee that the service providedis efficient due to the voting being based on humaninteraction.Wen et al. [20] proposed a dynamic cloud service trust

evaluation model that operates on service-level agree-ment and privacy-awareness. The proposed model hascomponents similar to [3] where trust is broken downinto various evaluations including direct, indirect, andreputation trust to calculate the final trust. The modelencompasses a dynamic trust update mechanism to

consistently update direct trust and deliver a dataset tothe user that is based on user preferences, satisfaction,accuracy, and feasibility. This approach can be inaccur-ate due to the user feedback mechanism being inconsist-ent. Rizvi et al. [21] proposed a trust model where a setof third-party auditors are utilized to establish trust be-tween CSUs and CSPs using both direct and indirecttrust characteristics. Specifically, the role of different au-ditors is to evaluate the security strength of CSPs usinga pre-defined criterion and provide a fair third-party as-sessment that can be later used to produce a trust value.Although their approach well justified the advantages ofthird-party assessment, they did not provide any mech-anism to use that as an assessment. Thus, they were un-able to compute a final trust score for each CSP.Alruwaythi et al. [22] presents the effects of user be-

havior in modeling trust which is associated when evalu-ating the principles between different sets of trustmodels. User behavior is considered in this model wherethe principles include expired, recent, and abnormal userbehavior. Using these principles, trust value either de-creases or increases depending on the number of mali-cious behaviors that have been detected. The evidence ofuser behavior is calculated through evidence, logins, op-erations, reliability, and performance. This trust model isunreliable because it is completely dependent on userbehavior which makes accuracy sporadic as the behaviorwould have to be of an exemplary user. Chen et al. [23]proposed a trust model that is similar to [22] as it isbased on user behavior; however, the priority is to en-sure safety of users which makes the main focus on de-grees of security. Recommendation, direct, andcomprehensive trust are calculated by interaction be-tween users and other cloud users along with theweighted average method. The proposed model, beingbased on user behavior, monitors abnormal behaviormore effectively by calculating the trust value basedupon cloud user behavior. This model has its benefits in-cluding the fact that the trust value can change with theenvironment because it is based on user input ratherthan generalization. However, abnormal users affect thesecurity and trust value. Werff et al. [24] proposed anexperimental analysis approach to cloud trust. The ex-perimental analysis includes various cloud solutions thatexplore the influence of information to consumers whenthey are choosing a CSP. A survey was conducted toconclude that positive and negative labels on cloud trustaffect the selection of user chosen CSPs. The fault of thisexperiment, to be used when evaluating trust, is that thesample of people being surveyed could not be largeenough to detect a consistent pattern in choosing theright CSPs for different users.Xu et al. [25] incorporates quality-of-service (QoS)

data to determine which CSPs are reliable and the effect


that unreliable QoS data can have on the criteria forCSP selection. The model presents a cloud service selec-tion framework that is based on a reputation approach.This approach encompasses a new user reputation cal-culation named MeURep. The algorithms follow param-eters based on QoS such as robustness, parallelization,and efficiency. However, this model does not considerother factors that are pertinent in choosing a CSP suchas security, reliability, etc. that have been mentioned inprevious trust models. Lu et al. [26] proposed a trust as-sessment model that is based on recommendations anddynamic self-adaptation in cloud services. Trust isbroken down into indirect and direct trust as exempli-fied by previous trust models. The recommendationmechanism is operated by third-party nodes that makerecommendations based on situations where it either ac-cepts or rejects circumstances after interaction. Thisprocess helps mitigate the risk of malicious node attackswhich, in turn, protect the cloud user and CSP. A disad-vantage to this approach is that it does not protectagainst more complex malicious attacks which can havea detrimental impact on the cloud user.Huang et al. [27] propose a trustworthy framework for

cloud security called the trustworthy computing frame-work (TwCF). The framework highlights the policiesthat are modified to adapt to a users’ security contents.The authors also utilize a trust management framework(TMF) that aids in decision-making of security policiesand the platform security mechanism (PSM) is used toensure that the correct security policies are being de-ployed. The authors highlight the fact that due to thelack of trust in cloud infrastructure, many CSPs are un-able to accurately determine their security status. Theneed for Tailored Trustworthy Spaces (TTS) to providean adaptive and flexible environment for a user is an im-portant component in securing trust between a CSU andCSP. The authors dissect TTS and combine it with mul-tiple security mechanisms to formulate a comprehensivecombination of trust and security in cloud infrastructure.While this approach of TTS and subjective trust is fun-damental in our proposed fuzzy-logic trust-based model,its disadvantage lies in the lack of fuzzy theory. Withoutthe analysis of the grey areas, it is hard for a CSU tomake a truly accurate decision when choosing a CSP.Kurdi et al. [28] introduce a lightweight trust manage-ment algorithm that is based upon subjective logic(InterTrust) in order to enhance trust in interconnectedcloud computing environments. The authors conductedan experiment that portrays the InterTrust capability ofproducing trust information efficiently in comparison toother algorithms and subjective trust without an algo-rithm. Subjective logic is explained in the literature as aproponent of trust that has an input of uncertainty andincomplete knowledge. The authors conclude that the

InterTrust algorithm is a possible tool that can be usedfor trust management for a multitude of cloud environ-ments. While certain components of this research areutilized within our fuzzy-logic based trust model, suchas subjective trust, it is not concentrated on the securityof cloud environments and mostly has its focus on prov-ing the superiority of InterTrust as solely a trust man-agement system.Within our proposed model, we have utilized the work

from the Mamdani inference system [29] in order toperform fuzzification using their inference rules to pro-vide an efficient application of human reasoning as partof our proposed model. We have also used Kurdi et al.[28] to identify our subjective and objective trust defini-tions. Other cited works within our proposed modelwere not utilized explicitly but taken into account byanalyzing their limitations to suggest a solution to thesedisadvantages. Our proposed model is unique comparedto other related works because we consider the inputfrom cloud service users and their definition of trust tothen synthesize the information into a coherent compu-tational form, and finally determining the trust level andreputation of a cloud service provider. After the trustlevel is evaluated it can be used for cloud service usersto determine which service would best fit their pros-pected needs.

Fuzzy-logic based trust modelIn the proposed fuzzy-logic based trust model, the secur-ity of a CSP is evaluated based on several influential fac-tors using a fuzzy inference system. Security is ofteninfluential when determining the trustworthiness of aCSP since data protection is one of the primary concernsfor enterprises that are still hesitant to migrate to thecloud. The steep learning curve associated with cloudcomputing can result in uncertainties and ambiguitiesamong CSUs. This leads to inaccurate decision makingand a lack of trust when CSUs attempt to choose themost efficient CSP in terms of security. Therefore, weimplement a fuzzy inference system to allow for an ac-curate selection of CSPs based on the security evaluationperformed by the CSU. We used the Mamdani inferencesystem [29] over the Takagi and Sugeno method [30]since it is more efficient for applying intuition and hu-man reasoning with the knowledge of experts as dis-cussed in [31]. Utilization of fuzzy CSU data allows ourproposed model to take into account the multiple possibil-ities or uncertainties that a CSU may have and quantifythat data in order to achieve a coherent security analysisof a CSP that would be catered to a CSU. Figure 1 illus-trates the basic building block of the inference processthat is used to calculate the security index of a CSP. It isshown that the CSU provides numerical (crisp) input, orfuzzy expressions, that are sent and converted into fuzzy


quantities (fuzzification). Rules are then applied to thefuzzy set to generate fuzzy output (inference engine), andfurther converted from fuzzy output to crisp output(defuzzification). The inference process of Fuzzification,Inference Engine, and Defuzzification are further elabo-rated in the subsequent subsections.

FuzzificationFuzzification is the process of transforming crisp valuesinto fuzzy values. Specifically, the crisp data provided bythe CSU is mapped to a fuzzy set which contains themembership functions and linguistic values [32, 33]. Afuzzy set is defined by the members it contains as shownin x ∈ X where x is the element. The symbol ∈ demon-strates that x is a member of the specified set X. In par-ticular, a fuzzy set is defined by ordered pairs as shownbelow:

A ¼ x;ËcA xð Þð Þ j x∈Uf g ð1Þ

where U is the universe of discourse which contains allof the elements that may be used in fuzzy set A. Themembership functions are read as μA(x) wherein amembership grade is assigned in the closed interval [0,1]to each element in U. We utilize this formula in our pro-posed model in order to take the CSU fuzzy input forthe process of fuzzification. Figure 1 shows the simpli-fied fuzzy inference process that we have utilized in ourfuzzy-logic model in order to process the information ofthe CSU.In our proposed approach, the linguistic variables rep-

resent the fuzzy sets fi (i = h, r, a, g, e) which consist ofharmful, risky, average, great, and exceptional. Each CSUcould interpret the meaning of these variables differ-ently. Therefore, the membership functions areemployed in the fuzzy sets to define the meaning of thelinguistic variables. In our approach, CSUs can utilizethese linguistic variables in order to make a decision onwhich CSP would better fit their security needs. Table 1shows the membership degrees of the variables. Once

the values are assigned, the linguistic variables canbe used by the CSUs to help evaluate the security ofthe CSPs.There are multiple factors that affect the security of a

CSP which can be evaluated by the CSU to perform anoverall security assessment. We identify four factorsused in our fuzzy-logic approach: compliance, accesscontrols, auditability, and encryption. For simplification,we do not utilize the sub-factors in our approach due tothe dependence and relationship among the aforemen-tioned factors. Our proposed model uses compliance tomeasure the membership degree in which CSPs protecttheir CSU’s data, auditability in order to make sure theCSP has the adequate management controls to protectthe CSU, and encryption to ensure CSU data safety.Figure 2 illustrates the membership functions of the pos-sible CSU input. The linguistic variables correlate totheir respected membership degree with a correspondingdescription for clarification. These linguistic variablesare scaled on a membership degree with a range of [0,1]in order to eliminate vagueness and allow an infinitespectrum between membership degrees. Specifically, weuse the triangular membership functions in Fig. 2 thatcan be defined as follows:

μ xð Þ ¼x − ab − a

; if a≤x≤bx − cb − c

; if b≤x≤c

8<:

9=; ð2Þ

The triangular membership function requires three pa-rameters represented as a, b, and c which dictates the

CSU Input(Crisp)

Quantitative Output(Crisp)Fuzzy

SetFuzzy Output

FuzzificationInference Engine

Defuzzification

Fuzzy Inference Process

Mapping Rules

Fig. 1 Overview of Fuzzy Inference System

Table 1 Definition of Linguistic Variables

Linguistic Variables Membership Degree Description

Exceptional 80–100 Near flawless

Great 60–90 Better than most

Average 30–70 Uncertain about security

Risky 10–40 Potentially harmful

Harmful 0–20 Presents a danger


three corners of the triangle. For instance, triangle (x;30, 50, 70) in Fig. 2 is the representative of the aver-age fuzzy set. First, compliance is an important secur-ity issue in cloud computing since non-compliancecan facilitate threats that propagate to the other se-curity factors. The governing bodies and internationalstandard entities that represent the foundation ofcloud security require compliance from the CSP.Without compliance, these entities are ineffective toprevent and mitigate the damage caused by an at-tacker. Furthermore, service level agreements (SLAs)and relevant policies/procedures that describe the per-formance, availability, payment, and delivery will beineffective if the CSP does not comply with its ownpolicies [11]. Compliance risks contribute to an inad-equate security index-score as well as the enlargementof distrust between the CSUs and CSPs. Another in-fluential security factor is the access controls used bythe CSP. Thus, the authentication mechanisms are avital component for maintaining the confidentialityand integrity of data.In addition, many attacks are aimed at exploiting

the access controls of the cloud such as phishing at-tacks. Malicious insiders pose a constant threat toCSUs since the insiders often possess authenticationand authorization credentials. Auditability refers tothe CSPs undergoing security assessments on

themselves which include performing employee back-ground checks, analyzing the access controls, and ini-tiating the vulnerability scans. This will help toprevent malicious insider attacks and unauthorizedusers from gaining access to cloud services. Moreover,the cloud is a dynamic environment where modifica-tions are made frequently and could promote add-itional security threats. Thus, the CSP should providean auditing service to monitor security. Data encryp-tion is another important security requirement thatwill restore the confidence in CSUs since data privacyand protection are among the primary concerns. If aCSP can provide the option for encrypting the data ofa CSU at rest and in transit, it will increase the reli-ability of the CSP and mitigate future threats such asdata breaches or losses.

Inference engineThe inference engine is responsible for applying the in-ference rules to the fuzzy input in order to generate thefuzzy output. In particular, the inference rules are usedto evaluate the linguistic values and map them to a fuzzyset which requires the defuzzification process to trans-form it into a crisp value. One of the fundamental con-cepts of the Mamdani method [29] is the inference ruleswhich provide the computation functionality of the sys-tem. These rules can be based on previous experiences,

Fig. 2 Membership Functions of Linguistic Variables


observations, and the knowledge of experts. Each fuzzyinference rule consists of two concepts which includethe if-then statements and the linguistic variables. Theif-then rules contain the antecedents and the conse-quence, where the linguistic input from the CSU is theantecedent and the consequence is the linguistic outputbased on the input.When fabricating an inference rule, operators such as

“and,” “or,” and sometimes “not” are used [30]. Theamalgamation of operators is referred to as the t-norms.The definition of the fuzzy “and” operator is given as:

μA∩B xð Þ ¼ min μA xð Þ; μB xð Þ½ � ð3ÞThe membership in class A is specified as μA while μB

represents the membership in class B. This rule extractsthe minimum number of the membership values of thefuzzy sets to compute the “and” operation. The fuzzy“or” operator is defined as:

μA∪B xð Þ ¼ max μA xð Þ; μB xð Þ½ � ð4ÞIn both eqs. (3) and (4), the x represents the degrees of

the membership functions that correspond to the fuzzysets. For example, A(x) refers to the membership degrees

of fuzzy set A. The “or” operation is computed byextracting the maximum value of the membershipvalues of the fuzzy sets. When formulating our infer-ence rules, we used the “and” operator since theevaluation factors are dependent on each other. The“or” operator is mostly used for independent factorsthat are not closely coupled. The operators allow forthe CSU to compute a fuzzy output distribution basedon the rule strength. The rule strength enables theaggregation of the fuzzy outputs into a distribution.We present several of the rules used in our evaluationof cloud security in linguistic form (see Table 2). Thefull abbreviation of each variable used in establishedrules is presented in Table 3.

DefuzzificationIn defuzzification, the fuzzy output of the inference engineis mapped to a crisp value that provides the most accuraterepresentation of the fuzzy set [31]. The classifications forthe fuzzy output are identical to the linguistic variablesused for the fuzzy input as shown in Table 1. The fuzzyoutputs are represented as Gi(i = h, r, a, g, e) and share thesame membership functions. The crisp output is obtainedin our proposed fuzzy approach by using the centroidmethod [34], which is defined below:

Table 2 Rules for Evaluating the Service Providers Security using Linguistic Form

Rule Description Rule Description

1 if hCP≙Hi∧hAC≙Hi∧hAU≙Hi∧hEC≙Hi →

thenhSC≅Hi

2 if hCP≙AVi∧hAC≙AVi∧hAU≙AVi∧hEC≙Ri →

thenhSC≅AVi

3 if hCP≙AVi∧hAC≙Hi∧hAU≙Hi∧hEC≙Hi →

thenhSC≅Hi

4 if hCP≙Gi∧hAC≙Hi∧hAU≙Hi∧hEC≙Hi →

thenhSC≅Hi

5 if hCP≙EXi∧hAC≙Hi∧hAU≙Hi∧hEC≙Hi →

thenhSC≅Hi

6 if hCP≙Ri∧hAC≙Ri∧hAU≙Hi∧hEC≙Hi →

thenhSC≅Hi

7 if hCP≙Ri∧hAC≙Ri∧hAU≙Ri∧hEC≙Hi →

thenhSC≅Ri

8 if hCP≙Ri∧hAC≙Ri∧hAU≙Ri∧hEC≙Ri →

thenhSC≅Ri

9 if hCP≙AVi∧hAC≙Ri∧hAU≙Ri∧hEC≙Ri →

thenhSC≅Ri

10 if hCP≙AVi∧hAC≙AVi∧hAU≙Ri∧hEC≙Ri →

thenhSC≅Ri

11 if hCP≙AVi∧hAC≙AVi∧hAU≙AVi∧hEC≙Ri →

thenhSC≅AVi

12 if hCP≙AVi∧hAC≙AVi∧hAU≙AVi∧hEC≙AVi →

thenhSC≅AVi

13 if hCP≙Gi∧hAC≙AVi∧hAU≙AVi∧hEC≙AVi →

thenhSC≅AVi

14 if hCP≙Gi∧hAC≙Gi∧hAU≙AVi∧hEC≙AVi →

thenhSC≅AVi

15 if hCP≙Gi∧hAC≙Gi∧hAU≙Gi∧hEC≙AVi →

thenhSC≅Gi

16 if hCP≙Gi∧hAC≙Gi∧hAU≙Gi∧hEC≙Gi →

thenhSC≅Gi

17 if hCP≙EXi∧hAC≙Gi∧hAU≙Gi∧hEC≙Gi →

thenhSC≅Gi

18 if hCP≙EXi∧hAC≙EXi∧hAU≙Gi∧hEC≙Gi →

thenhSC≅Gi

19 20 if hCP≙EXi∧hAC≙EXi∧hAU≙EXi∧hEC≙EXi →

thenhSC≅EXi


z ¼

Xnj¼1

z jμc z j� �

Xnj¼1

μc z j� � ð5Þ

The centroid method uses the center of mass, which isrepresented as z, in a fuzzy output distribution to deter-mine a single scalar value. The membership of thefuzzy sets is presented in uc, whereas the value of themembership is represented as zj. Finally, the crispoutput from the defuzzifier is an approximation thatis used to represent the security index of a CSP basedon the evaluation of the top-level factors by the CSU.The CSU can then use the index of a CSP to reviewif the security of the CSP is sufficient enough fortheir needs.

Experimental evaluation using case studiesIn this section, we present three case studies thatdemonstrate the effectiveness of our proposed fuzzy-logic approach. The first case entails a single evalu-ation of a CSP from the viewpoint of a CSU whereasthe second case encompasses the self-evaluation of aCSP by multiple CSUs. Lastly, the third case presentsa CSU who is trying to choose the most secure CSPfor data migration and ranks them according to thesecurity index. For the case study evaluations, we as-sume that the fuzzy sets described in the previoussection will be utilized. In addition, the universe ofdiscourse U = {0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50,55, 60, 65, 70, 75, 80, 85, 90, 95, 100} is discrete, andthe membership degrees for each fuzzy set are shownin Table 4. The evaluation factors are abbreviated asfollows: compliance = CP, access control = AC, audit-ability = AU, and encryption = EC.

Case 1 – CSU evaluating security strength of a CSPIn this scenario, a CSU has already been using thecloud to archive important data. However, the extent

Table 3 Abbreviations used in Established Evaluations Rules

Access Control AC

Auditability AU

Average AV

Compliance CP

Encryption EC

Exceptional EX

Great G

Harmful H

Security SC

Table 4 Fuzzy Set Classifications

Name Elements in Set Membership Degrees

Harmful (fh) 0 1

5 .75

10 .5

15 .25

20 0

Risky (fr) 10 0

15 .33

20 .66

25 1

30 .66

35 .33

40 0

Average (fa) 30 0

35 .25

40 .5

45 .75

50 1

55 .75

60 .5

65 .25

70 0

Great (fg) 60 0

65 .33

70 .66

75 1

80 .66

85 .33

90 0

Exceptional (fe) 80 0

85 .25

90 .5

95 .75

100 1

Table 5 Membership of Input Values

Evaluation Factors Input Values Fuzzy Sets Membership Degrees

Compliance 15 Harmful .25

Risky .33

Access Control 35 Risky .33

Average .25

Auditability 10 Harmful .5

Encryption 75 Great 1


of cloud security risks are increasing and poses aconstant threat to anyone outsourcing the informa-tion to a CSP. This causes the CSP to become un-trustworthy to the CSU. To determine if their CSPis trustworthy, the CSU evaluates the security of theCSP based on the previously identified security fac-tors to ensure that their data is protected. Supposethe input values are CP = 15, AC = 35, AU = 10, andEC = 75. Table 5 shows the corresponding member-ship values and fuzzy sets.Let us assume that there are two rules which fulfill the

conditions stated in Table 5 which are:

if CP≙Hi∧ AC≙Ri∧ AU≙Hi∧hhhEC≙Gi →

then

�SC≅Rih

if CP≙Ri∧ AChh ≙AV i∧ AU≙Hi∧hEC≙Gi →

then

�SC≅AV ih

For the above two evaluation rules, Eq. (3) isused to compute the “and” operators and createthe fuzzy output set for each rule as demonstratedbelow:

μsecurity ¼ f r ¼ min½μc ¼ f h 15ð Þ; μac ¼ f r 35ð Þ; μau

¼ f h 10ð Þ; μe ¼ f g 75ð Þ� ¼ min :25; :33; :50; 1½ � ¼ :25ð6Þ

μsecurity ¼ f a ¼ min½μc ¼ f r 15ð Þ; μac ¼ f a 35ð Þ; μau

¼ f h 10ð Þ; μe ¼ f g 75ð Þ� ¼ min :33; :25; :50; 1½ � ¼ :25ð7Þ

The membership degrees generated by the fuzzyinference rules are used to determine the rulestrength for each given rule. The CSU can aggregatethe fuzzy outputs into a distribution which can thenbe used for extracting the security index score. Fig-ure 3 illustrates the rule strength of the risky fuzzyoutput set. Figure 4 displays the rule strength forthe average fuzzy output set and shows the inter-connection between the risky and average fuzzy setsportrayed by the arrow. Furthermore, Fig. 5 showsthe output aggregation that forms from both fuzzyoutput sets at a closer view from Fig. 4.Once the output distribution is fabricated, the

CSU can use Eq. (5) to extract the security index ofthe CSP. According to the centroid method [34],the final security index score can be computed asfollows:

Fig. 3 Rule Strength for Risky Fuzzy Set


Fig. 4 Rule Strength for Average Fuzzy Set

Fig. 5 Output Distribution of Both Fuzzy Sets


With the security index score, the CSU can decideif the given CSP offers a sufficient level of securityto protect the customer’s information in the cloudstorage from threats and attacks. Since a securityindex of 39.06 is not exceptionally well, the CSUmay want to change their CSP in order to receivebetter protection from other potential service pro-viders. This particular case study has shown that ourproposed fuzzy approach can be effectively used by aCSU that is uncertain about the level of security of-fered by a CSP. .

Case 2 – self-security assessment by CSPSuppose that a CSP wants to evaluate its ownsecurity strength. In order to prevent personalbias from affecting the security index, the CSPpromotes a security evaluation of itself from theCSUs. Let us assume that there are three CSUsthat are chosen to evaluate the security of theCSP using our fuzzy-logic approach. The CSUsperform their evaluations separately and

discontinue the approach once they complete thefuzzy inference process. Each CSU presents onefuzzy output set which consists of the input valueand the membership degree that was computedbased on the fuzzy inference rules. The fuzzy out-put sets are received as Gg = (85, .33), Ge = (90, .5),and Ga = (65, .25). These fuzzy values are used bythe CSPs to compute a crisp security index. TheCSPs continue the fuzzy approach by aggregatingthe fuzzy output values into an output distribu-tion. Figure 6 displays the fuzzy output distribu-tion which will be used for the defuzzificationprocess. The graph of the output distribution inFig. 6 is sectioned in increments of five to be uti-lized in the Security Index as shown by the greenlines between the input values from 30 to 100.Next, the centroid method is used to extract thesecurity index from the center of the newlyformed output distribution. Using this method,the aggregated security index score can be com-puted as follows:

Security Index SIð Þ ¼ 10þ 15þ 20þ 25þ 30þ 35þ 40ð Þ � 0:25þ 30þ 35þ 40þ 45þ 50þ 55þ 60þ 65þ 70ð Þ � 0:25ð Þ½ �:25� 7þ 0:25� 9 ¼ 39:06

Fig. 6 Case 2 Output Distribution


The CSP has obtained a security index of 72.34 whichproves that the security level is better than most of theother CSPs. This case study has shown that our fuzzyapproach is beneficial for both the CSU and CSP. It canbe used for self-evaluation and to motivate a CSP to im-prove its security compliance, auditability, encryption,service quality, etc. Furthermore, the CSP can calculate amore accurate security index if more CSUs are providingthe fuzzy output sets. The computed aggregated securityindex score can be used by the CSP to advertise its se-curity strength including the compliance and other rele-vant information so that more cloud customers can beattracted.

Case 3 – ranking the CSPsLet us consider that a CSU desires to have the most effi-cient CSP in terms of security. In addition, there are sev-eral CSPs that appear to have a credible securitypresence. Our proposed fuzzy approach allows the CSUto evaluate each CSP and rank them according to the se-curity index. Suppose that the CSU has computed thefuzzy output sets for five different CSPs named CSP1,CSP2, CSP3, CSP4, and CSP5. The output sets for eachCSP are as follows: CSP1 = {(80, .66), (95, .75)}, CSP2 ={(95, .75), (90, .5)}, CSP3 = {(85, .33), (65, .33)}, CSP4 ={(90, .5), (70, .66)}, CSP5 = {(95, .75), (85, .25)}. Table 6displays the CSP fuzzy output values in accordance tothe output fuzzy sets. By using Eq. (5), the CSU can cal-culate the security indexes (SI) for the selected CSPs.

Each SI corresponds to a particular CSP in the followingmanner: SI1 = CSP1, SI2 = CSP2, SI3 = CSP3, SI4 = CSP4,and SI5 = CSP5. The computation of security score forCSP1 is as follows:

SI1 ¼ 60þ 65þ 70þ 75þ 80þ 85þ 90ð Þ � 0:66þ 80þ 85þ 90þ 95þ 100ð Þ � 0:75ð Þ½ �0:66� 7þ 0:75� 5 ≜81:72

The CSU then computes the second SI given as:

SI2 ¼ 80þ 85þ 90þ 95þ 100ð Þ � 0:75þ 80þ 85þ 90þ 95þ 100ð Þ � 0:5ð Þ½ �0:75� 5þ 0:5� 5 ≜90

The third calculation of security score is computed as:

SI3 ¼ 60þ 65þ 70þ 75þ 80þ 85þ 90ð Þ � 0:33þ 60þ 65þ 70þ 75þ 80þ 85þ 90ð Þ � 0:33ð Þ½ �0:33� 7þ 0:33� 7 ≜75

The fourth calculation of security score yields:

SI4 ¼ 80þ 85þ 90þ 95þ 100ð Þ � 0:5þ 60þ 65þ 70þ 75þ 80þ 85þ 90ð Þ � 0:66ð Þ½ �0:5� 5þ 0:66� 7 ≜80:27

Finally, the fifth security index computation is asfollows:

SI5 ¼ 80þ 85þ 90þ 95þ 100ð Þ � 0:75þ 80þ 85þ 90þ 95þ 100ð Þ � 0:25ð Þ½ �0:75� 5þ 0:25� 5 ≙90

The results of this security index computation aresummarized in Table 7. The SIs allow the CSU to rankthe CSPs based on their security strength and determinethe most secure CSP. Table 7 shows the ranking orderof the CSPs based on the computations performed earl-ier using Eq. (5). There is a tie for first place which canbe resolved with a larger universe of discourse used forthe membership functions. A higher set of numbers forpossible input values promotes a more accurate SI.These rankings identify reliable and secure CSPs for theCSUs while encouraging improvement among the CSPsto increase their ranks.

Security Index SIð Þ ¼ 30þ 35þ 40þ 45þ 50þ 55þ 60þ 65þ 70ð Þ � 0:25þ 60þ 65þ 70þ 75þ 80þ 85þ 90ð Þ � 0:33 80þ 85þ 90þ 95þ 100ð Þ � 0:5ð Þ½ �0:25� 9þ 0:33� 7þ 0:5� 5 ¼ 72:34

Table 6 Fuzzy Output Classification of each CSP

Name Input Values Fuzzy Sets Membership Degrees

CSP1 80 Great 0.66

95 Exceptional 0.75

CSP2 95 Exceptional 0.75

90 Exceptional 0.05

CSP3 85 Great 0.33

65 Great 0.33


70 Great 0.66


85 Exceptional 0.25

Table 7 Rankings of the CSPs

Rank Name Security Index

1 CSP2 90

1 CSP5 90

3 CSP1 81.72

4 CSP4 80.27

5 CSP3 75


Trust level validation of cloud service providerThe CSP validation requires that the fundamental secur-ity factors (such as access control, auditability, and en-cryption) must satisfy the customer’s securitypreferences. The trust-value can be calculated based onthe level of satisfaction a CSU receives from a given ser-vice provider. More specifically, we derive the closed-form expressions for the trust-level for each of these im-portant factors in the next subsequent subsections.

Trust-level auditabilityThe trust-level for auditability of a CSP can be deter-mined by using two types of characteristics. First, a CSPmay receive a high reputation (or we can refer to itsranking as stated earlier in the previous section) if it istrusted by a large number of CSUs. Second, that reputa-tion can be used by CSUs to make informed decisionsfor choosing cloud services from one or more CSPs.However, the second characteristic seems to be moreimportant than the first one since it allows the CSU tomake rational decisions to select cloud services. We de-rive the closed-form expression for the trust-level audit-ability of CSP using the second type of characteristics byapplying the following theorem:

Theorem 1: In our proposed fuzzy-logic approach (asdiscussed in Section 3), CSUs provide the crisp data asan input that can be mapped to a fuzzy set during theFuzzification process. For this Theorem 1, we refer to thiscrisp data as sample input data provided by a CSU. Wesample input data for an audit if all the samples givento the auditor are correctly aligned with the service levelagreement (SLA). If so, then the CSU is considered a reli-able party.Proof: First, let us prove that the chosen samples of

CSUs given to auditors for checking the trust-level of aCSP are correctly aligned with the SLA. This impliesthat if the given samples of services are properly stored

on the server of the CSP, the confirmation process ofsamples versus original service according to SLA can beexpressed as follows:

Tp←e Δβ;Δγð Þ∈Cuð8Þ

In (8), CSUs give the sample of services (few attributesof used service) to an auditor to verify the SLA.

V ¼Yni¼1

e ti;Uð Þ≅eYni∈Q

i Cuð Þ∈e Δβ;Δγð Þ !

ð9ÞIn (9), the total number of CSPs and its provided

cloud services to CSUs according to SLA, including theidentity of CSUs, is considered.

V ¼Yni¼1

e ti;Uð Þ:X∞j¼1

e Δ∂ð Þk∀⊂≈eYni∈Q

i Cuð Þ∈e Δβ;Δγð Þ !

ð10ÞEquation (10) shows the matching process, in which a

CSP checks the provided services to CSUs, and the audi-tor compares the services with the provided samples.

V ¼Yni¼1

e ti;Uð Þ ≈ eYnj¼0

i Cuð Þ !

ð11ÞEquation (11) confirms the matching process of sam-

ples with the original services given to CSUs. Based onour analytical model we can identify the trust-level ofCSPs and its broader impact. Table 8 shows the usednotions and their descriptions.

Results and discussionWe analyze the performance of the proposed fuzzy logicfor CSPs using the fuzzy inference system to show thatthe CSPs are trustworthy. We focus on the auditability, ac-cess control, and encryption in this experiment. The ex-periment involves the MATLAB version 8.5 with an IntelCore i3 Processor running at 2.40GHz with 4GB RAM.We used four physical servers. Two servers are set for theCSP which provide the web service and database service.The third server works as the third-party auditor (TPA)and the fourth server works as the CSUs. We consider thedata collection that is significant for the fuzzy-logic model.Thus, the fuzzy-logic model with fuzzy inference featuresshould be trained using training data to specify the great-est possibility for obtaining the required results. The useddata was collected using cloud users and cloud experts.

Table 8 Notations and their definitions for trust-level validationof CSPs

Notations Definitions

Tp Auditor that gets a sample from CSU

Cu CSU gives the sample to validate the trust of CSP

Δβ Service level agreement done between CSUs and CSP

Δγ Number of samples to examine

e Checkup process

ti Total services

k Number of CSUs

U Total number of CSUs on the server

V Validation of CSUs and provided services

Δ∂ CSP collects the money for this service


We used the Zoomerang online survey tool to collectdata sets from different sites. The survey consisted ofthe research questions showing the values for the mostsignificant variables which were chosen to represent theaccess control, encryption, and auditability. The pro-posed fuzzy-logic model uses different input fuzzy setsand five fuzzy sets for the parameters of output (e.g.,harmful, risky, average, great, and exceptional). After set-ting up the input and output fuzzy sets, the first step inthe simulation is to focus on the Fuzzification to be con-verted to input membership functions. This process isdone by applying the membership function editor avail-able in MATLAB. Each variable used in the experiment

is quantified into harmful, risky, average, great, and ex-ceptional for auditability, access control, and encryption.In Fig. 7, auditability, encryption, and access control

are detected to ensure the trust-level of the CSPs. Theperformance of these three key variables remains un-stable during the different time periods (days). The per-formance initially is satisfactory from day ten to twenty,but subsequently the performance is affected for allthree key variables (encryption, authorization, and accesscontrol). The reason for the decline of performancecould be the technical side including the obsolete pro-gramming languages, poor design and architecture, andthe lack of knowledge and technology support by CSPs.

Fig. 7 a showing the auditability; b the encryption and c access control of CSPs on different periods of times


In these experiments, we assigned the weightage forthese values to determine the realistic behavior of CSP.In Fig. 7, 7, and 7a, b, c, we have the average trends ofeach key variable. The lines in Fig. 7, 7, 7a, b, c visualizethe fluctuation between auditability, encryption, and ac-cess control. The black dotted line allows the reader tosee the average trend for each key variable and make acomparison between the variable and its average value.We observed that access control has an 81.8% maximumaverage trend that is more than other key variables. Anaverage trend validates that the access control of theCSPs remains better than authentication and encryption.However, the combined performance of all three keyvariables for identifying the trust-level of CSPs is de-creased, which is depicted in Fig. 8. We noticed that thecombined trust-level of CSPs was around 77.1% duringthe evaluation period.

ConclusionIn this paper, we presented a fuzzy-logic approach forevaluating the security of a CSP. Our approach addressesuncertainties associated with measuring trust, which issignificant because the goal of security evaluation is torestore the confidence of the CSU. It is essential tomaintain trust between the CSU and CSP to facilitatethe more widespread adoption of cloud computing andsimilar distributed systems. Our approach is intuitiveand easy to understand so that it can be used for a

variety of purposes. In the future, to get an even moreconcise evaluation of a CSP by evaluating the sub-factors of security, a CSU can choose the most trust-worthy CSP. Furthermore, our fuzzy approach can beused by the CSP for performing a self-evaluation andidentifying any areas of risk that might require improve-ment. The validity of the fuzzy-logic approach is con-firmed through case studies where real-life scenarios aretested. We hope to extend our work in the future by in-cluding the sub-factors of the evaluation factors used inour fuzzy-logic approach. This would improve the accur-acy and precision of the security index. In addition, weanticipate many new factors besides security, whichcould be used to determine the most trustworthy CSP.Moreover, we want to implement a weighting systemthat can be included in our fuzzy-logic approach. Theability to apply weights to certain factors wouldemphasize those factors and allow CSUs to have theirpreferences make an impact on the security index. Weplan to apply our proposed approach to case studies forspecific CSPs for security evaluation. This is importantfor CSPs to establish trust and build a reputation to gar-ner more CSUs. Our fuzzy-logic model can also be ac-companied with machine learning in order to learntechniques to make an accurate assessment of a CSPthat would be favoring to the CSU. This would allowCSPs to develop an even more efficient way of cateringto their CSUs.

Fig. 8 Trust-level of CSPs based on the three key variables: auditability, encryption, and access control


AcknowledgementsNot applicable.

Authors’ contributionsAll authors have equal contribution. Specifically, Dr. Rizvi has worked on thefirst four sections of the paper with three other authors (John Mitchell,Mohammad R. Rizvi and Iyonna Williams). Dr. Razaque has worked onSection 5 to validate the trust level of service providers using extensivesimulation and numerical analysis. All authors read and approved the finalmanuscript.

FundingThis research project was not funded.

Availability of data and materialsAll findings of this research work are presented in this article.

Competing interestsThe authors declare that they have no competing interests.

Author details1Department of Information Sciences and Technology, Pennsylvania StateUniversity, Altoona, PA 16601, USA. 2Department of Computer Science, NewYork Institute of Technology, New York, NY, USA. 3PricewaterhouseCoopers(PWC), Dallas, TX, USA.

Received: 30 November 2019 Accepted: 15 July 2020

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http://doras.dcu.ie/22321/1/Individual_Trust_and_the_Internet_Repository.pdfhttp://doras.dcu.ie/22321/1/Individual_Trust_and_the_Internet_Repository.pdfhttps://doi.org/10.1109/ICSSA.2015.014https://doi.org/10.1109/ICSSA.2015.014https://doi.org/10.1186/s13677-019-0129-8

AbstractIntroductionRelated workFuzzy-logic based trust modelFuzzificationInference engineDefuzzification

Experimental evaluation using case studiesCase 1 – CSU evaluating security strength of a CSPCase 2 – self-security assessment by CSPCase 3 – ranking the CSPs

Trust level validation of cloud service providerTrust-level auditabilityResults and discussion

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A fuzzy inference system (FIS) to evaluate the security ......RESEARCH Open Access A fuzzy inference...

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