 Original research
 Open Access
 Published:
Optimal alternative for suitability of Sboxes to image encryption based on mpolar fuzzy soft set decisionmaking criterion
Journal of the Egyptian Mathematical Society volume 28, Article number: 15 (2020)
Abstract
Our aim in this work is to improve the design and model of reallife applications. We put forward a standard based on mpolar fuzzy soft set decisionmaking criterion to examine the optimal alternative for the suitability of Sboxes to image encryption applications. The proposed standard studies the results of correlation analysis, entropy analysis, contrast analysis, homogeneity analysis, energy analysis, and mean of absolute deviation analysis. These analyses are applied to wellknown substitution boxes. The algorithm of outcomes of these analyses is additional observed and a mpolar fuzzy soft set decisionmaking criterion is used to decide the optimal alternative for suitability of Sbox to image encryption applications. All results taken by using the reality values for all Sboxes and experimental problems with reality values are discussed to show the validity of the optimal alternative for the suitability of Sbox to image encryption.
Introduction
The block ciphers (symmetric key cryptosystem) present an essential job in the area of secure communications. The security of an encryption algorithm is related to the performance of the building block which is liable for producing uncertainty in the cipher. This functionality is attained by the use of an Sbox, so this component is like a nucleus in an atom [1]. The perfection in the properties of an Sboxes has been a major problem of research in the area of cryptology. In this paper, we show the correlation analysis, entropy analysis, contrast analysis, homogeneity analysis, energy analysis, and mean of absolute deviation analysis for existing Sboxes. The correlation analysis is widely used to analyses the Sbox’s statistical properties [2]. The entropy analysis is a statistical method used to measure the uncertainty in image data. The amount of uncertainty in an encrypted image characterizes the texture of the image. In contrast analysis [3], the intensity difference between a pixel and its neighbor over the whole image is calculated. The elevated values of contrast analysis reflect the amount of randomness in encrypted images and results in enhanced security. The measure of closeness in the distribution of greylevel cooccurrence matrix (GLCM) elements to the GLCM diagonal is calculated by the use of homogeneity analysis [4]. The GLCM is the tabulation of how often different combinations of pixel brightness values (grey levels) occur in an image [5]. In another method, energy analyzes the sum of squared elements in the GLCM. This analysis provides the merits and demerits of various Sboxes in terms of energy of the resulting encrypted image. The final method that we implement on the encrypted image is the mean of absolute deviation (MAD) analysis [6]. This analysis determines the difference in the original and an encrypted image. There are numerous emerging encryption methods recently proposed in the literature. Although these algorithms appear to be promising, their robustness is not yet established and they are evolving to become standards. Some of these algorithms worth mentioning are the publickey cryptosystems based on chaotic Chebyshev polynomials [7], the advanced encryption standard (AES) cryptosystem using the features of mosaic image for extremely secure high data rate [8], and image encryption via logistic map function and heap tree [9]. The most common methods used to analyze the statistical strength of Sboxes are the correlation analysis, linear approximation probability, differential approximation probability, strict avalanche criterion, etc. We have included the correlation method as a benchmark for the remaining analysis used in this work. With the exception of correlation analysis, the application and use of the results of statistical analysis, presented in this paper, have not been applied to evaluate the strength of Sboxes. The correlation analysis, entropy analysis, contrast analysis, homogeneity analysis, energy analysis, and mean of absolute deviation analysis are performed on AES [10], APA [11], Gray [1], Lui J [12], residue prime [13], S8 AES [14], SKIPJACK [15], and Xyi [16] Sboxes. The results of these analyses are studied by the proposed criterion, and a fuzzy soft set decision is reached by taking into account the values of all the analysis on the different Sboxes. On the other hand, Majumdar and Samanta [17] presented the concept of generalized fuzzy soft sets, followed by studies on generalized multifuzzy soft sets [18], generalized intuitionistic fuzzy soft sets [19, 20], generalized fuzzy soft expert set [21], and generalized intervalvalued fuzzy soft set [22]. Recently, Zhu and Zhan [23] proposed the concept of fuzzy parameterized fuzzy soft sets, along with decisionmaking. Zhao et al. [24] presented a novel decisionmaking approach based on intuitionistic fuzzy soft sets. Deli [25] introduced the notion of intervalvalued neutrosophic soft sets and its decisionmaking. Fatimah et al. [26, 27] extended models include Nsoft sets, and hybrid models include intervalvalued fuzzy soft sets and (dual) probabilistic soft sets. In view of these developments, we will highlight the notion of possibility mpolar fuzzy soft set, which can be seen as a new possibility mpolar fuzzy soft model. Figures 1 and 2 explain the image before and after encryption.
Preliminaries and basic definitions
Wang et al. [28] provided the settheoretic operators and various properties of SVNSs. Ye [29, 30] proposed a multiattribute decisionmaking (MADM) method using the correlation coefficient under singlevalued neutrosophic environment. Ye [31, 32] further developed clustering method and decisionmaking methods by similarity measures of SVNS. Meanwhile, Peng and Dai [33] presented a new similarity measure of SVNS and applied them to decisionmaking. Biswas et al. [34] extended the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method for multiattribute singlevalued neutrosophic decisionmaking problem. Sahin and Kucuk [35] defined a subsethood measure for SVNS and applied to MADM. Evaluation based on distance from average solution (EDAS), originally proposed by Ghorabaee et al. [36], is a new MADM method for inventory ABC classification. It is very useful when we have some conflicting parameters. In the compromise MADM methods such as TOPSIS and VIKOR [37], the best alternative is got by computing the distance from ideal and nadir solutions. The desirable alternative has lower distance from ideal solution and higher distance from nadir solution in these MADM methods. Ghorabaee et al. [38] extended the EDAS method to supplier selection. As far as we know, however, the study of the MADM problem based on EDAS method has not been reported in the existing academic literature. Hence, it is an interesting research topic to apply the EDAS in MADM to rank and determine the best alternative under the singlevalued neutrosophic soft environment. Through a comparative analysis of the given methods, their objective valuation is carried out, and the method which maintains consistency of its results is chosen. For computing the similarity measure of two SVNSs, we propose a new axiomatic definition of the similarity measure, which takes in the form of SVNN. Comparing with the existing literature [31, 32, 39, 40], our similarity measure can remain more original decision information. By means of level soft sets, Feng et al. [41] presented an adjustable approach to fuzzy soft sets based decisionmaking. By considering different types of thresholds, it can derive different level soft sets from the original fuzzy soft set. In general, the final optimal decisions based on different level soft sets could be different. Thus, the newly proposed approach is, in fact, an adjustable method which captures an important feature for decisionmaking in an imprecise environment: some of these problems are essentially humanistic and thus subjective in nature. As far as we know, however, the study of the singlevalued neutrosophic soft MADM problem based on level soft set has not been reported in the existing academic literature. Considering that different attribute weights will influence the ranking results of alternatives, we develop a new method to determine the attribute weights by combining the subjective elements with the objective ones. This model is different from the existing methods, which can be divided into two tactics: one is the subjective weighting evaluation methods and the other is the objective weighting determine methods, which can be computed by grey system theory [42]. Figures 1 and 2 explain the image before and after encryption. For more information about mpolar fuzzy sets and analyses of SBox in image encryption applications based on fuzzy decisionmaking criterion.
The remainder of this paper is organized as follows: firstly, Sections 1 and 2 introduced some background of image encryption, showed and analyzed the types of the Sboxes. AES, APA, Gray, Lui J, residue prime, S8 AES, SKIPJAC, and Xyi talked about the properties of these Sboxes (the correlation analysis, entropy analysis, contrast analysis, homogeneity analysis, energy analysis, and mean of absolute deviation). Also, these sections explained soft set, fuzzy soft set, and fuzzy polar soft set.
In Section 4, the analyses of SBox in image encryption applications based on fuzzy sets and 2polar fuzzy soft set decisionmaking criterion are studied, in problem statement chosen suitability of Sbox to image encryption based on polar fuzzy soft set and construct an algorithm for a decisionmaking.
In Section 4.2, we developed a study to state decisionmaking based on 2polar fuzzy soft set by using two measures. Also, in Section 4.2, the optimal alternative for the suitability of Sbox to image encryption based on 2polar fuzzy soft set and by using a join and meet for 2polar fuzzy soft set are introduced. All the results are taken by using the reality values for all Sboxes, and experimental examples with reality values are discussed to show the validity of the proposed concept.
Section 5 is the conclusion and remarks.
Soft sets and mpolar fuzzy soft set
Let E be a nonempty finite set of attributes (parameters, characteristics, or properties) which the objects in U possess and let P(U) denote the family of all subsets of U. Then a soft set is defined with the help of a setvalued mapping as given below:
Definition 2.1 (Molodtsov [43] A pair (F, A) is called a soft set over U, where A ⊆ E and F : A → P(U) is a setvalued mapping. In other words, a soft set (F, A) over U is a parameterized family of subsets of U where each parameter e ∈ A is associated with a subset F(e) of U . The set F(i) contains the objects of U having the property i and is called the set of iapproximate elements in (F, A).
Definition 2.2 (Chen, Li and Koczy, [44, 45]) Elements ([0, 1]^{m})^{X} the set of all mappings from X to [0, 1]^{m} with the point – wise order are called an mpolar fuzzy sets, such that m is an arbitrary cardinality. A subset \( \mathcal{A}={\left\{{\mathcal{A}}_k\right\}}_{k\in K}\subseteq {\left({\left[0,1\right]}^m\right)}^X \) (or a mapping \( \mathcal{A}:K\to {\left({\left[0,1\right]}^m\right)}^X \) satisfying \( \mathcal{A}(k)={\mathcal{A}}_k\kern0.75em \forall k\in K \) ) is called an an mpolar fuzzy soft set on X.
Example 2.1 Let X = {a_{1}, a_{2}} be a two element set, I = {i_{1}, i_{2}, i_{3}} be a fourelement set, the 2polar fuzzy soft set \( \mathcal{A}\in {\left[{\left({\left[0,1\right]}^2\right)}^X\times {\left({\left[0,1\right]}^2\right)}^X\right]}^I \) defined by:
Definition 2.3 Let \( {\left\{{\mathcal{A}}_k\right\}}_{k\in K}\in {\left[{\left({\left[0,1\right]}^m\right)}^X\right]}^{I_k}. \) Define mpolar fuzzy soft sets
\( \bigvee {\left\{{\mathcal{A}}_k\right\}}_{k\in K}=\max\ {\left\{{\mathcal{A}}_k\right\}}_{k\in K} \) and \( \bigwedge {\left\{{\mathcal{A}}_k\right\}}_{k\in K}=\min\ {\left\{{\mathcal{A}}_k\right\}}_{k\in K} \) .
Analyses of Sbox in image encryption applications based on fuzzy sets and 2polar fuzzy soft set decisionmaking criterion
Proposed methodology and implementation
We chose the n×n Sboxes (AES, APA, Gray, Lui J, Residue Prime, S8 AES, SKIPJACK, and Xyi) used in popular block ciphers to do analysis. (n=2, 3, 4, 5…). (In Table 1 and figure. The reality values Entropy, contrast, average eneragy, eneragy, homogeneity, mad of prevailing Sbox are explained ).
Our aim is to examine the optimal alternative for suitability of Sboxes to image encryption. Correlation information plays the main role in stating the similarity of pixel patterns in the given image and its encrypted version by the use of techniques such as entropy analysis, contrast analysis, homogeneity analysis, energy analysis, and mean of absolute deviation analysis on the image. Now, we want to use the concept of soft set to choose the best Sbox, so assume that we analyze Sboxes (AES, APA, Gray, Lui J, Residue, S8 AES, SKIPJACK, and Xyi ). There are form the set X = {x_{1}, x_{2}, x_{3}, x_{4}, x_{5}, x_{6}, x_{7}, x_{8}, x_{9}} of alternatives where x_{i}{i = 1, 2, 3, 4, 5, 6, 7, 8, 9) are Sboxes and the alternatives x_{i}. To evaluate the Sboxes, we take the parameters I = {i_{1}, i_{2}, i_{3}, i_{4}, i_{5}, i_{6}} where i_{1} stands for entropy, i_{2} stands for contrast, i_{3} stands for average correlation, i_{4} stands for energy, i_{5} stands for homogeneity, i_{6} stands for MAD. These parameters are important with degree. (In Table 2 and Fig. 2 we explained the important values of Entropy, contrast, average eneragy, eneragy, homogeneity, mad of prevailing Sbox).
The data provided by the committee for decisionmaking use is the following 3polar fuzzy soft set \( \mathcal{A}\in {\left[{\left({\left[0,60\right]}^3\right)}^I\right]}^X={\left[{\left({\left[0,60\right]}^3\right)}^X\right]}^I={\left({\left[0,60\right]}^3\right)}^{I\times X}={\left({\left[0,60\right]}^3\right)}^{X\times I} \) defined by:
where \( \mathcal{A}\left({x}_1\right)\left({i}_1\right)=\left(6.6733,\mathrm{6.4325,6.7325}\right) \) means that the entropy of Sbox of x_{1} is given by group 1 (resp., by group 2, by group 3 ) is 6.6733 (resp., 6.4325,6.7325); meanings of \( \mathcal{A}\left({x}_s\right)\left({i}_t\right) \) can be explained similarly (s = 1, 2, 3, 4, 5, 6, 7, 8, 9; t = 1, 2, 3, 4, 5, 6).
To find the best choice from X, let us first compute the 3polar fuzzy set \( \overline{\mathcal{A}}\in {\left({\left[0,60\right]}^3\right)}^X \), defined by \( {p}_k{}^{\circ}\overline{\mathcal{A}}=60\wedge {\sum}_{i\in I}{p}_k{}^{\circ}\mathcal{A}(x)\kern1.25em \left(\forall x\in X\right),\kern0.5em \) where p_{k} : [0, 60]^{3} → [0, 60] is the kthe projection (k=1,2,3).
p_{1}(x_{1}) = 60 ∧ (6.6733 + 0.2455 + 0.8771 + 0.2917 + 0.9334 + 0.0000) = 9.0210. Similarly, (in Table 3, and Fig. 3 explained The kthe projection (k=1,2,3)).
Therefore
Based on the weight vector e^{→} = (8.00, 1.00,60.00 )^{T}. We compute the score \( S(x)=\mathcal{A}(x){e}^{\to } \) for each x ∈ X x ∈ X. Then: (in Table 4, and Fig. 4 we define score \( S(x)=\mathcal{A}(x){e}^{\to } \) for each x ∈ X)
As S(x_{4}) = (52.7318, 60, 52.7318)e^{→} = 52.7318 × 8.00 + 60 × 1.00 + 52.7318 × 60.00 =3645.7
As Sbox x_{4} (Lui J ) have the highest value, the best choice by experts should be Sbox x_{4} (Lui J ) is the suitability of Sbox to image encryption based on mpolar fuzzy soft set. Now, we will construct an algorithm for a decisionmaking problem as indicated below.
Suitability of Sboxes to image encryption based on 2polar fuzzy soft set
In this part, we study analyses Sboxes (APA, Liu J, Prime, S8). There are form the set X = {x_{1}, x_{2}, x_{3}, x_{4}} of the alternatives where x_{i}{i = 1, 2, 3, 4) are Sboxes and the alternatives x_{i}. To evaluate the Sboxes, we take the important alternative parameters I = {i_{1}, i_{2}, i_{3}} where i_{1} stands for contrast, i_{2} stands for energy, i_{3} stands for homogeneity. These parameters are important with degree (0.99, 0.89, and 0.92). Considering their own needs, the data for the optimal alternative for the suitability of Sbox to image encryption based on 2polar fuzzy soft set \( \mathcal{A}\in {\left[{\left({\left[0,1\right]}^2\right)}^X\times {\left({\left[0,1\right]}^2\right)}^X\right]}^I={\left[{\left({\left[0,1\right]}^4\right)}^X\right]}^I={\left[{\left({\left[0,1\right]}^4\right)}^I\right]}^X={\left({\left[0,1\right]}^4\right)}^{X\times I} \) defined by two managers to state the measure of the parameters give us the following data
Where,
\( \mathcal{A}\left({x}_1\right)\left({i}_1\right)=\left\langle \left(\mathrm{0.66,0.55}\Big),\Big(\mathrm{0.88.0.77}\right)\right\rangle \) . Means that of the encryption x_{1} of the parameter i_{1} (contrast) in the aspects of increase takes the values 0.66 or decrease takes the values 0.55 and by the second measure, the increase takes the values 0.88 or decrease takes the values 0.77 respectively; the meaning of \( \mathcal{A}\left({x}_s\right)\left({i}_t\right) \) can be explained similarly (s = 1, 2, 3.4; t = 1, 2, 3 ).
To find the best choice from X, let us first compute the 2polar fuzzy set \( \overline{\mathcal{A}}\in {\left({\left[0,1\right]}^2\right)}^{X\times I} \)defined by
where p_{k} : [0, 1]^{2} → [0, 1] is the kthe projection (k=1,2);
\( \overline{\mathcal{A}}\left({x}_1\right)\left({i}_1\right)=\left(0.66\times 0.88\right)+\left(0.55\times 0.77\right)=1\wedge 1.0043=1 \); Similarly
Now, we compute \( {m}_i={\sum}_{k=1}^4\left({x}_k\right)(i),\kern0.75em x\in X,\kern0.75em \left(i=1,2,3\right) \) and compute \( {r}_i={\sum}_i^3{m}_i{m}_j\kern0.75em \left(j=1,2,3,4\right) \). (in Table 5, and Fig. 5 we calculated \( {r}_i={\sum}_i^3\left({m}_i{m}_j\ \right)\kern0.5em \left(j=1,2,3,4\right) \))
\( {r}_i={\sum}_i^3\left({m}_i{m}_j\ \right)\kern0.5em \left(j=1,2,3,4\right) \); then
r_{1} = (m_{1} − m_{1}) + (m_{1} − m_{2}) + (m_{1} − m_{3}) + (m_{1} − m_{4}) = (2.9697312 − 2.9697312) + (2.9697312−1.55081606)+ ( 2.9697312−2.83343) + (2.9697312−2.896594) = 1.62835354, Similarly,
r_{2}= −4.04730702 , r_{3} = 1.08314874, r_{4} = 1.33580474. Since the score S(x) = max r_{i}. Then, the maximum score is r_{1}=1.62835354 and the optimal alternative for the suitability of Sbox to image encryption based on 2polar fuzzy soft set is to select x_{1} (APA encryption ).
Motivated from the above problem, we give the following algorithm for decisionmaking problem (and the like):
Suitability of Sboxes to image encryption based on two operations (∧ and∨) polar fuzzy soft sets
In this section, we study the problem by using two operations (∧ and ∨) polar fuzzy soft sets. So, we give Sboxes (Residue, Gray, AES, SKIPJACK, and Xyi). There are form the set X = {x_{1}, x_{2}, x_{3}, x_{4}, x_{5}} of alternatives where x_{i}{i = 1, 2, 3, 4, 5) are Sboxes and the alternatives x_{i}. To evaluate the Sboxes, we take the important alternative parameters I = {i_{1}, i_{2}, i_{3}} where i_{1} stands for entropy, i_{2} stands for homogeneity, i_{3} stands for MAD. These parameters are important with degree (8.00, 1.0, and 60.0). Considering their own needs, the data for the optimal alternative for the suitability of Sbox to image encryption based on 2polar fuzzy soft set \( \mathcal{A}\in {\left({\left[0,60\right]}^{2\times 3}\right)}^{I\times X} \) defined by:
where \( \mathcal{A}\left({x}_1\right)\left({i}_1\right)=\left\{\left(\mathrm{6.6,6.5}\Big),\Big(\mathrm{7.6,5.7}\right),\left(\mathrm{7.6,5.8}\right)\right\} \) means that of the encryption x_{1} (Residue ) of the parameter i_{1} (entropy) increase and decrease of growth given by the first measure is the increase takes the value 6.6 and decrease takes the value 6.5 , by the second measure, the increase takes the value 7.6 and decrease takes the value 5.7, and by the third measure, the increase takes the value 7.6 and decrease takes the value 5.7 and decrease takes the value7.6 and decrease takes the value 5.8. Respectively; the meaning of \( \mathcal{A}\left({x}_s\right)\left({i}_t\right) \) can be explained similarly (s = 1, 2, 3.4,5; t = 1, 2, 3 ). Similarly,
A subset \( \mathcal{B}={\left\{{B}_i\right\}}_i:\mathrm{I}\to {\left({\left[0,60\right]}^{2\times 3}\right)}^X \) is called also 3polar fuzzy soft set on X, define by \( \mathcal{B}\left(\mathrm{i}\right)={B}_i\forall i\in I,\kern0.5em \)the data for optimal alternative for the suitability of Sbox to image encryption based on 3polar fuzzy soft set given by another three measures \( \mathcal{B}\in {\left[{\left({\left[0,60\right]}^{2\times 3}\right)}^X\right]}^I \) =([0, 60]^{2 × 3})^{X × I} defined by
Now, we need to find the best choice from X based on \( \mathbb{C}=\mathcal{A}\wedge \mathcal{B}. \) So, compute ℂ.
Secondly, compute the 3polar fuzzy soft set \( \hat{\mathbb{C}}\in {\left({\left[0,60\right]}^{2\times 3}\right)}^X, \) defined by
where p_{k} : [0, 60]^{2} → [0, 60] is the kthe projection (k=1, 2, 3);
\( \hat{\mathbb{C}}\left({x}_1\right)\left({i}_1,{j}_1\right)=(60)\wedge \left[\left(5.6\times 6.6\times 6.6\right)+\left(5.5\times 4.7\times 5.2\right)\right]=(60)\wedge (6096.21584)=60 \); Similarly; (in Table 6, and Fig. 6 we define the values of \( \hat{\mathbb{C}}(x)\left(i,j\right)\in {\left({\left[0,60\right]}^{2\times 3}\right)}^X \)).
Therefore,
Now, we make a decision by two ways:
 (1)
First way:
Define a mapping ℂ_{M} : X → R,by \( {\mathbb{C}}_M(x)={\sum}_{\left(i,j\right)\in {I}^2}\beta (x)\left(i,j\right), \) where
From the following table; (Table 7 and Fig. 7, we define the mapping ℂ_{M} : X → R, by \( {\mathbb{C}}_M(x)={\sum}_{\left(i,j\right)\in {I}^2}\beta (x)\left(i,j\right) \))
Since ℂ_{M}((x_{1}) = 120.5688 = max ℂ_{M},then the optimal alternative for the suitability of Sbox to image encryption X based on 3polar fuzzy soft set is x_{1} (Residue encryption).
Motivated from the above problem, we give the following algorithm for decisionmaking problem:

(2)
Second way:
Compute \( {m}_i={\sum}_{k=1}^5\left({x}_k\right)\left(i,j\right),\kern0.75em x\in X,\kern0.75em \left(i,j\right)\in \left(I\times I\right) \) and compute \( {r}_i={\sum}_{j=1}^9\ \left({m}_i{m}_j\right)\kern0.75em \left(i=1,2,3,4,5\right) \), then, (in Table 8 and Fig. 8 we compute \( {r}_i={\sum}_{j=1}^3\ \left({m}_i{m}_j\right)\kern0.75em \left(i=1,2,3,4,5\right) \))
r_{1} = (m_{1} − m_{1}) + (m_{1} − m_{2}) + (m_{1} − m_{3}) + (m_{1} − m_{4}) + (m_{1} − m_{5}) = (540 − 540) + (540 − 360.176) + (540 − 540) + (540 − 0.69656)+ (540 − 0.2156) = 1618.87224; similarity,
r_{2} = 359.79184, r_{3} = 1079.08784, r_{4} = − 1437.60536, r_{5} = − 1440.01016. Since r_{1} = 1618.87224 = max r_{i}, then the optimal alternative for the suitability of Sbox to image encryption X based on 3polar fuzzy soft set is x_{1} (Residue encryption).
Motivated from the above problem, we give the following algorithm for decisionmaking problem:
Now, find the optimal alternative for the suitability of Sbox to image encryption based on 3polar fuzzy soft set. By using the operator∨,
First, compute \( \mathbb{C}=\mathcal{A}\vee \mathcal{B}. \) So, compute ℂ.
Secondly, compute the 3polar fuzzy soft set \( \hat{\mathbb{C}}\in {\left({\left[0,60\right]}^{2\times 3}\right)}^X, \) defined by
Where p_{k} : [0, 60]^{2} → [0, 60] is the kthe projection (k=1, 2, 3);
\( \hat{\mathbb{C}}\left({x}_1\right)\left({i}_1,{j}_1\right)=(60)\wedge \left[\left(6.6\times 7.6\times 7.6\right)+\left(6.6\times 7.6\times 7.6\right)\right]=(60)\wedge (762.432)=60; \) Similarly, in Table 9, and Fig. 9 we compute \( \hat{\mathbb{C}}(x)\left(i,j\right) \))
Therefore,
Now, we make a decision by two ways:
 (1)
First way:
Define a mapping ℂ_{M} : X → R,by \( {\mathbb{C}}_M(x)={\sum}_{\left(i,j\right)\in {I}^2}\beta (x)\left(i,j\right), \) where
From the following table; (in Table 10 and Fig. 10, we compute ℂ_{M} : X → R, by \( {\mathbb{C}}_M(x)={\sum}_{\left(i,j\right)\in {I}^2}\beta (x)\left(i,j\right) \))
Since ℂ_{M}((x_{1}) = 120.1776 = max ℂ_{M},then the optimal alternative for the suitability of Sbox to image encryption X based on 3polar fuzzy soft set is x_{1} (Residue encryption).
Motivated from the above problem, we give the following algorithm for decisionmaking problem:

(2)
Second way:
Compute \( {m}_i={\sum}_{k=1}^5\left({x}_k\right)\left(i,j\right),\kern0.75em x\in X,\kern0.75em \left(i,j\right)\in \left(I\times I\right) \) and compute \( {r}_i={\sum}_{j=1}^9\ \left({m}_i{m}_j\right)\kern0.75em \left(i=1,2,3,4,5\right) \), then, (in Table 11, and Fig. 11, we compute \( {m}_i={\sum}_{k=1}^5\left({x}_k\right)\left(i,j\right),\kern0.75em x\in X,\kern0.75em \left(i,j\right)\in \left(I\times I\right) \) and in Table 12 and Fig. 12, compute \( {r}_i={\sum}_{j=1}^9\ \left({m}_i{m}_j\right)\ \left(i=1,2,3,4,5\right) \)).
r_{1} = (m_{1} − m_{1}) + (m_{1} − m_{2}) + (m_{1} − m_{3}) + (m_{1} − m_{4}) + (m_{1} − m_{5}) = (480.1776 − 480.1776) + (480.1776 − 480.08208) + (480.1776 − 480.0892) + (480.1776 − 480.0632) + (480.1776 − 480.083) = 0.39292; similarity,
r_{2} = − 0.08468, r_{3} = − 0.04908, r_{4} = − 0.11968, r_{5} = − 0.08008. Since r_{1} = 0.39292 = max r_{i}, then the optimal alternative for the suitability of Sbox to image encryption X based on 3polar fuzzy soft set is x_{1} (Residue encryption).
Motivated from the above problem, we give the following algorithm for decisionmaking problem:
Conclusion and remarks
The major contributions in this paper can be summarized as follows:
 1.
We arrive to put a standard of optimal alternative for suitability of Sboxes to image encryption based on mpolar fuzzy soft set.
 2.
A novel design and model of reallife applications and explained in the existing literature the ways to the best choice of alternative for the suitability of Sboxes.
 3.
Studies the results of correlation analysis, entropy analysis, contrast analysis, homogeneity analysis, energy analysis, and mean of absolute deviation analysis of all Sboxes
 4.
The algorithm of the outcomes of these analyses is additionally observed and a mpolar fuzzy soft set decisionmaking criterion is used to decide the optimal alternative for suitability of Sbox to image encryption applications.
In the future, we shall apply more advanced theories into
 1.
Intervalvalued fuzzy soft sets in stochastic multicriteria decisionmaking based on regret theory and prospect theory with combined weight;
 2.
Intervalvalued fuzzy soft sets in emergency decisionmaking based on WDBA and CODAS with new information measure;
 3.
Hesitant fuzzy soft decisionmaking based on revised aggregation operators, WDBA and CODAS,
 4.
Pythagorean fuzzy set decisionmaking based on Pythagorean fuzzy set
Availability of data and materials
No data were used to support this study.
References
M. T. Tran, D.K. Buil, and A. D. Doung. Gray for Sbox for advanced encryption standard,” First International Conference on Computer Science Engineering and Applications 2008, 253–256
L. Zhang, X. Liao, and X. Wang, Neural information processing, 18th International Conference, ICONIP 2011, Shanghai, China, November 1317, 2011, Proceedings, Part I
S. Y. Chen, W. C. Lin, and C.T. Chen, Graph. Models Imag. Proc. 1991, 453457
Jing, F., Li, M., Zhang, H., Zhang, B.: Unsupervised image segmentation using local homogeneity analysis,  Proceedings of. ISCAS. 2, 456 (2003)
E. S. Gadelmawla, Nondestr. Test. Eval. 2004 Int. 37, 577
Avcibas, I., Memon, N., Sankur, B.: IEEE Trans. Imag. Proc. 212–221 (2003)
Prasadh, K., Ramar, K., Gnanajeyaraman, R.: Public key cryptosystems based on chaotic chebyshev polynomials. Journal of Engineering and Technology Research. 1(7), 122–128 (2009)
G. M. Alam, M. L. Kiah, B. B. Zaidan, A. A. Zaidan, and H. O. Alanazi, A new hybrid module ., Int International. Journal . Phys. Sci. 2010, 5: 32543260
R. Enayatifar, International Journal Physics Science 2011, 216 221
J. Daemen and V. Rijmen, AES Proposal: Rijndael. AES Algorithm submission, Available: http://csrc.nist.gov/archive/aes/rijndael/Rijndaelammended. pdf (1999).
Cui, L., Cao, Y.: International Journal Innov. Comput. I3, 45 (2007)
J. Lui, B. Wai, X. Cheng, and X. Wang, Proc. International Conference on Computer Science information’s network applications. (2005). 05, 724
Abuelyman, E.S., Alsehibani, A.A.S.: IJCSNS International. J. Compute. Sci. Network. Security. 8, 304–309 (2008)
I. Husain, T. Shah, and H. Mahmoud, A new algorithm to construct secure keys for AES Int. J. Contemp. Math. Sciences, Vol. 5, 2010, no. 26, 1263  1270
Dueri, S., Maury, O.: Application of the APECOSME model to the skipjack tuna (Katsuwonus pelamis) fisheries of the Indian Ocean. Institute de Recherché pour le development, CRH Avenue Jean Monnet, France (1998)
Shi, X.Y., Xiao, H.U., You, X.C., Lam, K.Y.: American Journal of Applied Sciences. 8(8), 754–757 (2011)
Majumdar, P., Samanta, S.K.: Generalised fuzzy soft sets. Computers and Mathematics with Applications. 59, 1425–1432 (2010)
Pal, M., Dey, A.: Generalised multifuzzy soft set and its application in decision making. Pacific Science Review A: Natural Science and Engineering. 17, 23–28 (2015)
Agarwal, M., Biswas, K.K., Hanmandlu, M.: Generalized intuitionistic fuzzy soft sets with applications in decision making. Applied Soft Computing. 13, 3552–3566 (2013)
Khalil, A.M.: Commentary on “Generalized intuitionistic fuzzy soft sets with applications in decisionmaking”. Applied Soft Computing. 37, 519–520 (2015)
Hazaymeh, A.A., Abdullah, I.B., Balkhi, Z.T., Ibrahim, R.I.: Generalized fuzzy soft expert set. Journal of Applied Mathematics. 22, 328195 (2012)
Alkhazaleh, S., Salleh, A.R.: Generalised intervalvalued fuzzy soft set. Journal of Applied Mathematics. 18, 870504 (2012)
Zhu, K., Zhan, J.: Fuzzy parameterized fuzzy soft sets and decision making. International Journal of Machine Learning and Cybernetics. 7, 1207–1212 (2016)
Zhao, H., Ma, W., Sun, B.: A novel decision making approach based on intuitionistic fuzzy soft sets. International Journal of Machine Learning and Cybernetics. 8, 1107–1117 (2017)
Deli, I.: Intervalvalued neutrosophic soft sets and its decision making. International Journal of Machine Learning and Cybernetics. 8, 665–676 (2017)
Fatimah, F., Rosadi, D., Hakim, R.B.F., Alcantud, J.C.R.: Nsoft sets and their decision making algorithms. Soft Computing. 22(12), 3829–3842 (2018)
Fatimah, F., Rosadi, D., Hakim, R.B.F., Alcantud, J.C.R.: Probabilistic soft sets and dual probabilistic soft sets in decisionmaking. Neural Computing and Applications. 1–11 (2017). https://doi.org/10.1007/s005210173011y
Wang, H., Smarandache, F., Zhang, Y.Q., Sunderraman, R.: Single valued neutrosophic sets. Multispace Multistruct. 4, 410–413 (2010)
Ye, J.: Multicriteria decisionmaking method using the correlation coefficient under singlevalued neutrosophic environment. International Journal of General Systems. 42, 386–394 (2013)
Ye, J.: Improved correlation coefficients of single valued neutrosophic sets and interval neutrosophic sets for multiple attribute decision making. Journal of Intelligent and Fuzzy Systems. 27, 2453–2462 (2014)
Ye, J.: Clustering methods using distancebased similarity measures of singlevalued neutrosophic sets. Journal of Intelligent Systems. 23, 379–389 (2014)
Ye, J.: Multiple attribute group decisionmaking method with completely unknown weights based on similarity measures under single valued neutrosophic environment. Journal of Intelligent and Fuzzy Systems. 27, 2927–2935 (2014)
Peng, X.D., Dai, J.G.: Approaches to singlevalued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function. Neural Computing and Applications. https://doi.org/10.1007/s005210162607y
Biswas, P., Pramanik, S., Giri, B.C.: TOPSIS method for multiattribute group decisionmaking under singlevalued neutrosophic environment. Neural Computing and Applications. 27, 727–737 (2016)
Sahin, R., Kucuk, A.: Subsethood measure for single valued neutrosophic sets. Journal of Intelligent and Fuzzy Systems. 29, 525–530 (2015)
Ghorabaee, M.K., Zavadskas, E.K., Olfat, L., Turskis, Z.: Multicriteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatics. 26, 435–451 (2015)
S. Opricovic and G.H. Tzeng, Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS, European Journal of Operational Research 156 (2004), 445–455.
Ghorabaee, M.K., Zavadskas, E.K., Amiri, M., Turskis, Z.: Extended EDAS method for fuzzy multicriteria decision making: An application to supplier selection. International Journal of Computers Communications and Control. 11, 358–371 (2016)
R. S¸ Sahin and A. K.¨uc¸ ¨uk, On similarity and entropy of neutrosophic soft sets, Journal of Intelligent and Fuzzy Systems 27 (2014), 2417–2430.
Mukherjee, A., Sarkar, S.: Several similarity measures of neutrosophic soft sets and its application in real life problems. Annals of Pure and Applied Mathematics. 7, 1–6 (2014)
Fang, F., Jun, Y.B., Liu, X., Li, L.: An adjustable approach to fuzzy soft set based decision making. Journal of Computational and Applied Mathematics. 234, 10–20 (2010)
Liu, S.F., Dang, Y.G., Fang, Z.G.: Grey systems theory and its applications. Science Press, Beijing (2000)
Molodtsov, D.A.: Soft set theoryfirst results. Computers and Mathematics with Applications. 37, 19–31 (1999)
J. Chen, S.G. Li, S.Q. Ma and X. Wang, mPolar fuzzy sets: An extension of bipolar fuzzy sets, The scientific world journal 2014: 416530
Koczy, L.T.: Vectorial Ifuzzy Sets. In: Gupta, M.M., Sanchez, E. (eds.) Approximate Reasoning in Decision Analysis, North Holland, Amsterdam, pp. 151–156 (1982) [45] Akram, M., mPolar Fuzzy Graphs, Studies in Fuzziness and Soft Computing, Springer, 371(2019)
Acknowledgements
The author is very appreciative to the reviewers for their precious comments which enormously ameliorated the quality of this paper.
Funding
No place gives supporting or funding, only by the author.
Author information
Authors and Affiliations
Contributions
The author read and approved the final manuscript.
Corresponding author
Ethics declarations
Competing interests
The author declares that there is no competing interests.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
About this article
Cite this article
Khalaf, M.M. Optimal alternative for suitability of Sboxes to image encryption based on mpolar fuzzy soft set decisionmaking criterion. J Egypt Math Soc 28, 15 (2020). https://doi.org/10.1186/s427870200068z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s427870200068z
Keywords
 8 × 8 Sboxes
 Image encryption
 Homogeneity analysis
 Sboxes
 Decisionmaking criterion
AMS Classification
 03E72, 47S40