Bifurcation of a Fractional-Order Delayed Malware Propagation Model in Social Networks

Xu, Changjin; Liao, Maoxin; Li, Peiluan

doi:https://doi.org/10.1155/2019/7057052

Discrete Dynamics in Nature and Society

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Abstract Introduction Conclusions Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2019 | Article ID 7057052 | https://doi.org/10.1155/2019/7057052

Bifurcation of a Fractional-Order Delayed Malware Propagation Model in Social Networks

Changjin Xu,¹Maoxin Liao,²and Peiluan Li³

Academic Editor: Seenith Sivasundaram

Received06 Oct 2018

Revised01 Jan 2019

Accepted09 Jan 2019

Published20 Jan 2019

Abstract

In recent years, with the rapid development of the Internet and the Internet of Things, network security is urgently needed. Malware becomes a major threat to network security. Thus, the study on malware propagation model plays an important role in network security. In the past few decades, numerous researchers put up various kinds of malware propagation models to analyze the dynamic interaction. However, many works are only concerned with the integer-order malware propagation models, while the investigation on fractional-order ones is very few. In this paper, based on the earlier works, we will put up a new fractional-order delayed malware propagation model. Letting the delay be bifurcation parameter and analyzing the corresponding characteristic equations of considered system, we will establish a set of new sufficient conditions to guarantee the stability and the existence of Hopf bifurcation of fractional-order delayed malware propagation model. The study shows that the delay and the fractional order have important effect on the stability and Hopf bifurcation of considered system. To check the correctness of theoretical analyses, we carry out some computer simulations. At last, a simple conclusion is drawn. The derived results of this paper are completely innovative and play an important guiding role in network security.

1. Introduction

Nowadays, social networks are important platforms for disseminating information and building relationship. Different from the classical approaches of communication, social networks have fast speed of information propagation and diffusion. Furthermore, social networks have important effect on commercial negotiations, social connections, and information-sharing activities. Owing to the potential applications of social networks in many areas, many scholars pay much attention to dynamics of wireless sensor networks. For example, Deng et al.[1] considered the mobility-based clustering protocol of wireless sensor networks, Liu et al. [2] focused on the design and statistical analysis of a new chaotic block cipher for wireless sensor networks, Wang and Tseng [3] discussed the distributed deployment schemes for mobile wireless sensor networks, and Kulkarni and Zambare [4] studied the impact of Houseplants in purification of environment using wireless sensor network.

Malware software (malware) is an important tool to attack cybersecurity. Malware, which widely appears in the Internet, will cause some serious security risks of networks such as network paralysis, instability of society, loss of secret key, and personal information leakage. In order to understand and grasp the damage of malware, it is necessary for us to investigate the cause of malware occurrence, the harmful level to human beings, and the internal mechanism of malware propagation. During the past few decades, some researchers have obtained excellent achievements. For example, Liu et al. [5] analyzed the spread of malware with the influence of heterogeneous immunization. By constructing an appropriate Lyapunov function, the sufficient condition which guarantees the globally asymptotic stability of the equilibrium is obtained. Their research shows that interpretation of malware parameters and prediction of the evolution of future malware outbreaks play an important role in ensuring user safety. Signes Pont et al. [6] modelled the malware propagation in mobile computer devices. Hosseini and Azgomi [7] considered the stability of an SEIRS-QV malware propagation model in heterogeneous networks. In detail, one can see [7–11].

The fractional calculus is a generalization of ordinary differentiation and integration to random order (noninteger)[12–19]. Many scholars pointed out that it is more accurate to describe the real object by fractional-order derivatives than integer-order ones. In recent years, the fractional calculus has many potential applications in many areas such as robotics, bioengineering, electroanalytical chemistry, viscoelasticity, heat conduction, and economics [20, 21]. Thus, the dynamical nature of fractional-order differential systems has attracted much attention and excellent results have been achieved. We refer the readers to [22–28]. In particular, some achievements about Hopf bifurcation of fractional-order differential systems are also available. For example, Rajagopal et al. [29] studied the bifurcation and chaos of delayed fractional-order chaotic memfractor oscillator, Huang [30] dealt with the bifurcation in a delayed van der Pol oscillator, and Xiao et al. [31] discussed the bifurcation control of a fractional-order van der pol oscillator. In detail, one can see [29, 32–35].

Here we must point out that all the above works on Hopf bifurcation of fractional-order (integer-order) differential models are only concerned with neural networks and predator-prey models. Up to now, there are few articles that focus on the effect of delays on the Hopf bifurcation of fractional-order delayed malware propagation model.

In 2018, Du et al. [36] investigated the following malware propagation model with delay:where denotes the susceptible population, denotes the carrier population, denotes the infectious population, denotes recovered population, denotes the number of susceptible nodes, denotes the constant contact rate for , and , denotes the vaccination rate of nodes, denotes the rate constant for nodes becoming susceptible again, denotes the number of new recovered devices from carrier, and denotes the rate constant for nodes leaving for . When , where is a constant, then system (1) becomesStimulated by the analysis above, we modify (1) as the following fractional-order delayed malware propagation model:where . All the variables and coefficients have the same implications as those in (1).

The main objective of this article is to handle two problems: (i) the sufficient conditions that guarantee the stability and existence of Hopf bifurcation of system (3) are established; (ii) the effect of the delay and fractional order on Hopf bifurcation of model (3) is shown.

The highlights of this paper consist of four points:

(i) The integer-order delayed malware propagation model in social networks has been extended to fractional-order delayed malware propagation model in social networks, which can better describe the memory and hereditary properties of the model.

(ii) A sufficient criterion of the stability and the existence of Hopf bifurcation of fractional-order delayed malware propagation model in social networks are derived. The effect of delay and fractional order on the stability and Hopf bifurcation of (3) is illustrated.

(iii) To the best of our knowledge, there are no articles that focus on the Hopf bifurcation of fractional-order malware propagation model. The obtained results of this article will enrich and develop the Hopf bifurcation theory of fractional-order delayed differential equations and supplement the previous publications.

(iv) The idea of this manuscript will provide a good reference to investigate many other fractional-order systems with delays.

The rest of this paper is organized as follows. In Section 2, several notations and preliminary results on fractional calculus are listed. In Section 3, a sufficient condition to ensure the stability and the existence of Hopf bifurcation of system (3) is presented. The effect of the delay on the stability and Hopf bifurcation of the considered system (3) is discussed. In Section 4, some numerical simulations are carried out to check the theoretical findings. In Section 5, we give a brief conclusion.

2. Preliminary Results

In this section, we introduce three definitions and two lemmas.

Definition 1 (see [37]). The fractional integral of order for a function is defined as follows:where denotes the Gamma function and

Definition 2 (see [37]). The Caputo fractional-order derivative of order for a function is defined as follows: where and is a positive integer such that In particular, when ,

Definition 3 (see[32]). For the given fractional-order systemwhere is said to be the equilibrium point if

Lemma 4 (see [38]). Consider the following autonomous system where Let be the root of the characteristic equation of . Then system is asymptotically stable if and only if . In this case, each component of the states decays towards 0 like . Also, this system is stable if and only if and those critical eigenvalues that satisfy have geometric multiplicity one.

Lemma 5 (see [39]). For the given fractional-order delayed differential equation with Caputo derivative, , where . Then the characteristic equation of the system is If all the roots of the characteristic equation of the system have negative real roots, then the zero solution of the system is asymptotically stable.

3. Effect of the Delay on Bifurcation for Model (3)

In this section, we will investigate the impact of the delay on Hopf bifurcation for system (3).

Obviously, system (3) has an equilibrium point . If the following conditionholds, then system (3) has a positive equilibrium point whereIn the sequel, considering the biological meaning, we only consider the positive equilibrium point . Let and still denote by ; then system (3) takes the following form:The linearization of (10) near isThe corresponding characteristic equation of (11) is which leads to where where If , then (13) takes the following form: If the following condition holds, then all the roots of (16) satisfy . By Lemma 4, we can conclude that the equilibrium point of (3) with is locally asymptotically stable.

Let be a root of (13). Thenwhere By (18), we have which leads to where Let If the following condition holds, then ; then Eq. (21) has at least one positive real root. Thus, Eq. (13) has at least one pair of pure roots. In view of Sun et al. [40], we have the following result.

Lemma 6. For (13), the following results are true:
(1) If (Q2) holds and , then (13) has no root with zero real parts for all .
(2) If (Q3) holds, then (13) has a pair of purely imaginary roots when , where where , and is the unique positive zero of the function

The proof of Lemma 6 is similar to the proof of Lemma 3.1 in Sun et al. [40]. Here we omit it.

In order to obtain the transversality condition of Hopf bifurcation, the following hypothesis is given: where

Lemma 7. Let be the root of (13) at satisfying ; then

Proof. Differentiating (13) with respect to , one has Hence Then In view of (Q4), one has The proof of Lemma 7 is completed.

According to the analysis above and Lemmas 4 and 5, we have the following theorem.

Theorem 8. For system (3), if (Q1)-(Q4) hold, then the equilibrium point is locally asymptotically stable for and system (3) undergoes a Hopf bifurcation near the equilibrium point when

Remark 9. Xu et al. [41–47] studied the Hopf bifurcation of integer-order delayed models. In this article, we investigate the Hopf bifurcation of delayed malware propagation model. All the derived results and analysis ideas [41–47] cannot be applied to (3) to obtain the stability and the existence of Hopf bifurcation for (3). Although the authors of [36, 48] analyzed the Hopf bifurcation, and the stability of integer-order malware propagation model, they did not consider the fractional-order case. From the point, the fruit of this article on the stability and the existence of Hopf bifurcation for (3) is completely new and completes some earlier publications.

4. Numerical Simulations

Consider the following fractional-order system:where It is not difficult to obtain that system (32) has the positive equilibrium point . Let . Then the critical frequency and the bifurcation point . Then all the conditions (Q1)-(Q4) of Theorem 8 are satisfied. Figure 1 indicates that the positive equilibrium point of system (32) is locally asymptotically stable for . Figure 2 shows that system (32) loses its stability; Hopf bifurcation occurs when . The relation of fractional order on the critical frequency and bifurcation point of (32) is shown in Table 1.

5. Conclusions

In real world, malware plays an important role in economy and society. Based on the previous works, in this paper, we propose a new fractional-order delayed malware propagation model in social networks. By choosing the time delay as bifurcation parameter, we establish a sufficient condition to ensure the stability and the existence of Hopf bifurcation of fractional-order delayed malware propagation model. The research shows that the positive equilibrium point of the involved model is locally asymptotically stable when the time delay is less than the critical value , while, when the time delay exceeds the critical value , the system will lose its stability and Hopf bifurcation will appear. The investigation also shows the effect of the fractional order on the stability and Hopf bifurcation of involved model. Furthermore, the relationship of fractional-order and bifurcation point is discussed. The derived results have important theoretical significance and practical value in controlling the malware propagation.

Data Availability

The figure data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work is supported by National Natural Science Foundation of China (No. 61673008), Project of High-level Innovative Talents of Guizhou Province ([2016]5651), Major Research Project of The Innovation Group of The Education Department of Guizhou Province ([2017]039), Project of Key Laboratory of Guizhou Province with Financial and Physical Features ([2017]004), and Foundation of Science and Technology of Guizhou Province ([2018]1025 and [2018]1020).

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Copyright © 2019 Changjin Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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