Modeling the spread of computer virus via Caputo fractional derivative and the betaderivative
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Abstract
The concept of information science is inevitable in the human development as science and technology has become the driving force of all economics. The connection of one human being during epidemics is vital and can be studied using mathematical principles. In this study, a wellrecognized model of computer virus by Piqueira et al. (J Comput Sci 1:31−34, 2005) and Piqueira and Araujo (Appl Math Comput 2(213):355−360, 2009) is investigated through the Caputo and betaderivatives. A less detail of stability analysis was discussed on the extended model. The analytical solution of the extended model was solved via the Laplace perturbation method and the homotopy decomposition technique. The sequential summary of each of iteration method for the extend model was presented. Using the parameters in Piqueira and Araujo (Appl Math Comput 2(213):355−360, 2009), some numerical simulation results are presented.
Keywords
Computer virus Caputo fractional derivative Betaderivative Antidotal StabilityBackground
The idea of computer virus came into being around 1980 and has continued threatening the society. During these early stages, the threat of this virus was minimal [1]. Modern civilized societies are being automated with computer applications making life easy in the areas such as education, health, transportation, agriculture and many more. Following recent development in complex computer systems, the trend has shifted to sophisticate dynamic of computer virus which is difficult to deal with. In 2001, for example, the cost associated with computer virus was estimated to be 10.7 United State dollars for only the first quarter [1]. Consequently, a comprehensive understanding of computer virus dynamics has become inevitable to researchers considering the role played by this wonderful device. To ensure the safety and reliability of computers, this virus burden can be tackled in twofold: microscopic and macroscopic [2−6].
The microscopic level has been investigated by [3], who developed antivirus program that removes virus from the computer system when detected. The program is capable of upgrading itself to ensure that new virus can be dealt with when attacks computer. The characteristics of this program are similar to that of vaccination against a disease. They are not able to guarantee safety in computer network system and also difficult to make good future predictions. The macroscopic aspect of computer has seen tremendous attention in the area of modeling the spread of this virus and determining the longterm behavior of the virus in the network system since 1980 [4]. The concept of epidemiological modeling of disease has been applied in the study of the spread of computer virus in macroscopic scale [6−8].
Possibly reality of nature could be well understood via fractional calculus perspective. A considerable attention has been devoted to fractional differential equations by the fact that fractionalorder system is capable to converge to the integerorder system timely. Fractionalorder differential equations’ applications in modeling processes have the merit of nonlocal property [9, 10, 11]. The model proposed in [10] is a deterministic one and fails to have hereditary and memory effect and therefore, cannot adequately describe the processes very well.
In this paper, we present the fractionalorder derivative and obtain analytic numeric solution of the model presented in [10]. The rationale behind the application of fractional derivatives can also be ascertained from some of the current papers published on mathematical modeling [12, 13, 14, 15, 16]. In addition to this, the practical implication of fractional derivative can be established in [17].
Model formulation
In this study, we take into account the model proposed by [10]. In their study, the total population of this model is denoted by T which is subdivided into four groups. S denotes the noninfected computers capable of being infected after making contact with infected computer. A is the kind of computers noninfected equipped with antivirus. I denotes infected computers capable of infecting noninfected computers and R deals with removed ones due to infection or not. The recruitment rate of computers into the noninfected computers’ class is denoted by N and \(\mu\) is the proportion coefficient for the mortality rate which is not attributable to the virus. \(\beta\) is the rate of proportion of infection as a result of product of SI. The conversion of susceptible computer into antidotal is the product of SI denoted by \(\alpha _{SA}\). The proportion of converting infected computers into antidotal ones in the network is the product of SA denoted by \(\alpha _{IA}\). The rate of removed computers being converted into susceptible class is represented by \(\sigma\) and \(\delta\) denotes the rate at which the virus rate computers useless and remove from the system.
This provides the invariance of \(\Omega\) as to be determined. We conclude from this theorem, that it is sufficient to deal with the dynamics of (1) in \(\Omega\). In this respect, the model can be assumed as being epidemiologically and mathematically wellposed [18].
Basic concept about the betaderivative and Caputo derivative
Definition 1
Theorem 1
 1.
\(_0^A D_x^\alpha \left( {af(x) + bg(x)} \right) = a_0^A D_x^\alpha \left( {f(x)} \right) + b_0^A D_x^\alpha \left( {f(x)} \right)\) for all a and b being real numbers,
 2.
\(_0^A D_x^\alpha (c) = 0\) for c any given constant,
 3.
\(_0^A D_x^\alpha \left( {f(x)g(x)} \right) = g(x)_0^A D_x^\alpha \left( {f(x)} \right) + f(x)_0^A D_x^\alpha \left( {g(x)} \right)\)
 4.
\(_0^A D_x^\alpha \left( {\frac{{f(x)}}{{g(x)}}} \right) = \frac{{g(x)_0^A D_x^\alpha \left( {f(x)} \right)  f(x)D_x^\alpha \left( {g(x)} \right) }}{{g^2 x}}\)
The proofs of the above relations are identical to the one in [19].
Definition 2
Theorem 2
\(_0^A D_x^\alpha \left[ {_0^A I_x^\beta f(x)} \right] = f(x)\) for all \(x \geqslant a\) with f a given continuous and differentiable function.
Definition 3
Analysis of the mathematical model
Analysis of approximate solutions
One of the most challenging tasks in nonlinear fractional differential equation systems is probably how to obtain exact analytical solutions. This accounts for the reasons why in recent times, a lot of attention has been devoted in the quest for obtaining techniques that can ensure asymptotic solutions in such situations. We shall make reference to some of the recent techniques on this subject which are efficient and effective and have been widely used; for instance, the decomposition method [12], Sumudu homotopy perturbation method [20], the Adomian Decomposition method [11, 21], homotopy perturbation method [19, 22, 23], the homotopy Laplace perturbation method [24], and the homotopy. In this study however, we shall make use of two of these stated techniques, specifically the Laplace homotopy perturbation method and the homotopy decomposition method. The homotopy decomposition method will be employed to provide solution to the model with the betaderivative, followed by the Laplace homotopy perturbation method which will be used to solve the system with Caputo derivative.
Solution with the Laplace homotopy perturbation method

Algorithm 1. This technique can be employed to obtain a special solution to system (2) via a Caputo fractional derivative

Input \(p^0:\left\{ \begin{array}{l}S_0 (t) = S(0) \\ I_0 (t) = I(0) \\ R_0 (t) = R(0) \\ A_0 (t) = A(0) \quad \\ \end{array} \right.\) as initial input,

\(j\)number terms in the rough computation,
 Output: \(\left\{ {\begin{array}{*{20}l} {S_{{{\text{appr}}}} (t) = S(0)} \hfill \\ {I_{{{\text{appr}}}} (t) = I(0)} \hfill \\ {R_{{{\text{appr}}}} (t) = R(0)} \hfill \\ {A_{{{\text{appr}}}} (t) = A(0),} \hfill \\ \end{array} } \right.\quad {\text{the estimated solution}}\).Step 2: For \(j = 1\) to \(n1\) do step 3, step 4 and step 5:$$\begin{aligned} {\text{Step}}\mathrm{{ }}1:{\text{Put}}\left\{ \begin{array}{l} S_0 (t) = S(0) \\ I_0 (t) = I(0) \\ R_0 (t) = R(0) \\ A_0 (t) = A(0) \\ \end{array} \right. \mathrm{{ }}\quad {\text{and}}\quad\mathrm{{ }}\left\{ \begin{array}{l} S_{{\rm appr}} (t) = S(0) \\ I_{{\rm appr}} (t) = I(0) \\ R_{{\rm appr}} (t) = R(0) \\ A_{{\rm appr}} (t) = A(0) \\ \end{array} \right. = \mathrm{{ }}\left\{ \begin{array}{l} S_0 (t) = S(t) \\ I_0 (t) = I(t) \\ R_0 (t) = R(t) \\ A_0 (t) = A(t), \\ \end{array} \right. \end{aligned}$$(20)Step 3: Compute$$\begin{aligned} \left\{ \begin{array}{l} S_1 (t) = \ell ^{  1} \left\{ {\frac{1}{{\tau ^\alpha }}\ell \left( {  \alpha _{AS} S_0 A_0  \beta S_0 I_0 + \sigma R_0 } \right) } \right\} \\ I_1 (t) = \ell ^{  1} \left\{ {\frac{1}{{\tau ^\alpha }}\ell \left( {\beta S_0 I_0  \alpha _{AI} A_0 I_0  \delta I_0 } \right) } \right\} \\ R_1 (t) = \ell ^{  1} \left\{ {\frac{1}{{\tau ^\alpha }}\ell \left( {\delta I_0  \sigma R_0 } \right) } \right\} \\ A_1 (t) = \ell ^{  1} \left\{ {\frac{1}{{\tau ^\alpha }}\ell \left( {\alpha _{AS} S_0 A_0 + \alpha _{AI} A_0 I_0 } \right) } \right\} \\ \end{array} \right. \end{aligned}$$(21)Step 4: Compute$$\begin{aligned} \left\{ \begin{array}{l} S_n (t) = \ell ^{  1} \left\{ {\frac{1}{{\tau ^\alpha }}\ell \left( {  \alpha _{AS} \sum \limits _{j = 0}^{n  1} {A_{(n  j  1)} } S_j A_j  \beta \sum \limits _{j = 0}^{n  1} {I_{\left( {n  j  1} \right) } } S_j + \sigma R_{\left( {n  1} \right) } } \right) } \right\} \\ I_n (t) = \ell ^{  1} \left\{ {\frac{1}{{\tau ^\alpha }}\ell \left( {\beta \sum \limits _{j = 0}^{n  1} {I_{\left( {n  j  1} \right) } } S_j  \alpha _{AI} \sum \limits _{j = 0}^{n  1} {I_{\left( {n  j  1} \right) } } A_j  \delta I_{\left( {n  1} \right) } } \right) } \right\} \\ R_n (t) = \ell ^{  1} \left\{ {\frac{1}{{\tau ^\alpha }}\ell \left( {\delta I_{\left( {n  1} \right) }  \sigma R_{\left( {n  1} \right) } } \right) } \right\} \\ A_n (t) = \ell ^{  1} \left\{ {\frac{1}{{\tau ^\alpha }}\ell \left( {\alpha _{AS} \alpha _{AS} \sum \limits _{j = 0}^{n  1} {A_{(n  j  1)} } S_j A_j + \alpha _{AI} \sum \limits _{j = 0}^{n  1} {I_{\left( {n  j  1} \right) } } A_j } \right) } \right\} \\ \end{array} \right. \end{aligned}$$(22)Step 5: Compute$$\begin{aligned} \left\{ \begin{array}{l} S_{\left( {m + 1} \right) } (t) = B_m (t) + S_{({\rm appr})} (t) \\ I_{\left( {m + 1} \right) } (t) = B_m (t) + I_{({\rm appr})} (t) \\ R_{\left( {m + 1} \right) } (t) = K_m (t) + R_{({\rm appr})} (t) \\ A_{\left( {m + 1} \right) } (t) = K_m (t) + A_{({\rm appr})} (t) \\ \end{array} \right. \end{aligned}$$(23)Stop.$$\begin{aligned} \left\{ \begin{array}{l} S_{\left( {{\rm appr}} \right) } (t) = S_{{\rm appr}} (t) + S_{(m + 1)} (t) \\ I_{\left( {{\rm appr}} \right) } (t) = I_{{\rm appr}} (t) + I_{(m + 1)} (t) \\ R_{\left( {{\rm appr}} \right) } (t) = R_{{\rm appr}} (t) + R_{(m + 1)} (t) \\ A_{\left( {{\rm appr}} \right) } (t) = A_{{\rm appr}} (t) + A_{(m + 1)} (t) \\ \end{array} \right. \end{aligned}$$(24)
The above algorithm shall be applied to obtain the unique solution of system (3) via the Caputo derivative. We shall explore the situation where the betaderivative is used and this will be discussed in “Basic concept about the betaderivative and Caputo derivative” section.
Solution with the homotopy decomposition method
Input \(\left\{ {\begin{array}{*{20}l} {S_{0} (t) = S(0)} \\ {I_{0} (t) = I(0)} \\ {R_{0} (t) = R(0)} \\ {A_{0} (t) = A(0)} \\ \end{array} } \right.\quad {\text{as initial input}}\),
\(j\)number terms in the rough computation,
Output:\(\left\{ \begin{array}{l}S_{{\rm appr}} (t) \\ I_{{\rm appr}} (t) \\ R_{{\rm appr}} (t) \\ A_{{\rm appr}} (t) \quad\\ \end{array} \right.\) the estimated solution.
Numerical results
We shall employ both Algorithms 1 and 2 to obtain an approximate solution of system (3) via the Caputo fractional derivative and the betaderivative methods.
With the Caputo fractional derivative
With the betaderivative
Numerical simulations
Conclusions
The concept of betaderivative and Caputo fractional derivative has assisted in investigating the spread of computer virus in a system. This computer virus has been found all over the world where computers are available and causing major financial losses to many establishments. It is worthy to note that the definition of fractional derivative is associated with the convolution of the derivative of a given function with its function power. Convolution is applied to many branches of engineering including image processing as a filter. Fractional derivative, however, in epidemiology serves a memory capable of tracing the spread from beginning to the infected individual. For betaderivative which ranges between fractional order and local derivative, the spread of computer virus at local level is identified with a given fractional order. In this study, two distinct concepts of derivatives are employed to investigate the spread of computer virus. The proposed model based on the methodology used was solved iteratively. The numerical simulation results depict that the prediction is based on the fractional order of beta. Simply when beta is close to 1, we obtain nonrealistic prediction which is not the case in [10] and when beta is ≤0.5, a good prediction is attained. Since it is the desire of any institution to have their computers with virus free, the initial computers at the end of the simulation moved into Anodotal section when beta is <0.5 as observed in Figs. 4, 5, 6, 7 and 8. It worthy to notice that when beta is 1, we have ordinary differential derivative cases for both derivatives which do not provide a good prediction. Thus, with the newly introduced, betacalculus has the potential of providing a vivid account of physical problem more precisely.
Notes
Authors' contributions
All the authors have contributed equally for the production of this study. All authors read and approved the final manuscript.
Acknowledgements
The authors are thankful to the editor and reviewers for their careful reading and suggestion that greatly improved the quality of the paper.
Competing interests
The authors declare that they have no competing interests.
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