Table of Contents Author Guidelines Submit a Manuscript
Discrete Dynamics in Nature and Society
Volume 2015, Article ID 101874, 8 pages
http://dx.doi.org/10.1155/2015/101874
Research Article

Hopf Bifurcation of an SIQR Computer Virus Model with Time Delay

1Anhui University of Finance and Economics, School of Management Science and Engineering, Caoshan Road 962, Bengbu 233030, China
2Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China

Received 13 June 2014; Accepted 14 November 2014

Academic Editor: Muhammad Naveed Iqbal

Copyright © 2015 Zizhen Zhang and Huizhong Yang. 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.

Abstract

A delayed SIQR computer virus model is considered. It has been observed that there exists a critical value of delay for the stability of virus prevalence by choosing the delay as a bifurcation parameter. Furthermore, the properties of the Hopf bifurcation such as direction and stability are investigated by using the normal form method and center manifold theory. Finally, some numerical simulations for supporting our theoretical results are also performed.

1. Introduction

Recently, many scholars have been studying the prevalence of computer viruses by establishing reasonable mathematics models [15]. In [1], Piqueira and Araujo established a modified version of SIR model for the computer viruses in network and they got the stability and bifurcation conditions of the model. In [3], Gan et al. proposed an epidemic model of computer viruses by incorporating a vaccination probability in the SIRS model with generalized nonlinear incidence rate.

As is known, many computer viruses have different kinds of delays when they spread, such as latent period delay [6, 7], temporary immunity period delay [8], and other types [911]. In [6], Yang proposed the following SIQR computer virus model with time delay: where , , , and denote the numbers of nodes in states susceptible, infectious, quarantined, and recovered at time , respectively. is the new number of nodes. is the proportion of new nodes who are immunized directly. is the probability for a susceptible node to be infected. is the natural death rate of nodes. and are the death rates due to the virus for the nodes in states infectious and quarantined, respectively. , , and are the coefficients of state transmission. is the latent period of the virus.

Gan et al. investigated global attractivity and sustainability of system (1) in [3]. However, studies on dynamical systems not only involve a discussion of attractivity and sustainability but also involve many dynamical behaviors such as stability, bifurcation, and chaos. In particular, the existence and properties of the Hopf bifurcation for the delayed dynamical systems have been studied by many authors [810, 12]. In [8], Feng et al. investigated the Hopf bifurcation of a delayed SIRS viral infection model in computer networks by regarding the delay as a bifurcation parameter. In [12], Zhuang and Zhu investigated the Hopf bifurcation of an improved HIV model with time delay and cure rate. It is well known that the occurrence of Hopf bifurcation means that the state of virus prevalence changes from an equilibrium point to a limit cycle, which is not welcomed in networks. To the best of our knowledge, few papers deal with the research of Hopf bifurcation of system (1). Simulated by this reason and motivated by work above, we consider the Hopf bifurcation of system (1) in this paper.

This paper is organized as follows. In Section 2, we show that the complex Hopf bifurcation phenomenon at the positive equilibrium of the system (1) can occur as the delay crosses a critical value by choosing the delay as a bifurcation parameter. In Section 3, explicit formulae for the direction and stability of the Hopf bifurcation are derived by using the normal form theory and center manifold theorem. In Section 4, some numerical simulations are carried out to verify the theoretical results. A brief discussion is given to conclude this work in Section 5.

2. Stability and Existence of Local Hopf Bifurcation

In this section, we mainly focus on the local stability of positive equilibrium and existence of local Hopf bifurcation. It is not difficult to verify that if the basic reproduction number , system (1) has a unique positive equilibrium , where The linearization of system (1) about the positive equilibrium is where The characteristic equation of system (1) is where For , (7) reduces to where By the Routh-Hurwitz criterion, if condition   (10)–(15) holds, is locally asymptotically stable in the absence of delay: For , let be the root of (7). Then, we can get where Then, we can obtain the following equation with respect to : where Let ; then (15) becomes If the coefficients of system (1) are given, then one can get the roots of (17) by Matlab software package easily. In order to give the main results in this paper, we make the following assumption.

  Equation (17) has at least one positive root.

If condition holds, we know that (15) has at least a positive root such that (7) has a pair of purely imaginary roots . The corresponding critical value of the delay is where Substituting into the left side of (7) and taking the derivative with respect to , one can obtain Thus, where and .

Thus, if condition , then . According to the Hopf bifurcation theorem in [13], we have the following results.

Theorem 1. If the conditions hold, then the positive equilibrium of system (1) is asymptotically stable for ; system (1) undergoes a Hopf bifurcation at the positive equilibrium when and a family of periodic solutions bifurcating from the positive equilibrium near .

3. Direction and Stability of the Hopf Bifurcation

In this section, we investigate the direction of the Hopf bifurcation and the stability of the bifurcating periodic solutions by using the normal form theory and the center manifold theorem in [13].

Let , , , , and , and normalize the time delay by . Then system (1) can be transformed into an FDE as where where By the Riesz representation theorem, there is a matrix function with bounded variation components , such that In fact, we choose where is the Dirac delta function.

For , we define Then system (22) is equivalent to the following operator equation: Next, we define the adjoint operator of and a bilinear inner product where .

Let be the eigenvector of corresponding to and let be the eigenvector of corresponding to . From the definition of and and by a simple computation, we obtain From the definition of , we can obtain Then we choose such that , .

Following the algorithms given in [13] and using similar computation process in [14], we can get the coefficients which determine the direction and stability of the Hopf bifurcation: with where and can be determined by the following equations, respectively: with Then, we can get the following coefficients: In conclusion, we have the following results.

Theorem 2. For system (1), if (), then the Hopf bifurcation is supercritical (subcritical). If (), then the bifurcating periodic solutions are stable (unstable). If (), then the period of the bifurcating periodic solutions increases (decreases).

4. Numerical Simulation and Discussion

In this section, a numerical example is given to support the theoretical results in Sections 2 and 3. Let , , , , , , , , and . Then, we get a particular case of system (1): Then, we can get and the unique positive equilibrium of system (39). By some complex computation, it can be verified that condition is satisfied for system (39). Further, we obtain , . By Theorem 1, we can conclude that when the positive equilibrium is asymptotically stable. This property can be illustrated by Figures 1, 2, and 3. However, if we choose , the positive equilibrium becomes unstable and a Hopf bifurcation occurs, which can be illustrated by Figures 4, 5, and 6. Furthermore, we obtain , . Then, from (38) we get , , and . Therefore, we can know that the Hopf bifurcation of system (39) is supercritical, the bifurcated periodic solutions are stable, and the period of the periodic solutions increases according to Theorem 2.

Figure 1: The track of the states , , , and for .
Figure 2: The phase plot of the states , , and for .
Figure 3: The phase plot of the states , , and for .
Figure 4: The track of the states , , , and for .
Figure 5: The phase plot of the states , , and for .
Figure 6: The phase plot of the states , , and for .

In addition, according to the numerical simulation, we find that the onset of the Hopf bifurcation can be delayed by decreasing the number of new nodes connected to a network or increasing the immunization rate of the new nodes. Therefore, the managers of a real network should control the number of the new nodes connected to network and strengthen the immunization of the new nodes in order to delay and control the onset of the Hopf bifurcation, so as to make the propagation of computer viruses be predicted and controlled easily.

5. Conclusion

In this paper, the problem of Hopf bifurcation for a delayed SIQR computer virus model has been studied. The stability of the positive equilibrium and the existence of Hopf bifurcation under this model are analyzed. It has been found that when the delay is suitable small (), the computer virus mode is asymptotically stable. In this case, the characteristics of the propagation of computer viruses can be easily predicted and controlled. However, if the delay passes though the critical value , a Hopf bifurcation occurs. Then, the propagation of computer viruses becomes unstable and out of control. Furthermore, the properties of the Hopf bifurcation such as direction and stability have also been investigated in detail. Finally, numerical results have been presented to verify the analytical predictions.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

The authors are grateful to the anonymous referees and the editor for their valuable comments and suggestions on the paper. The research was supported by the National Nature Science Foundation of China (61273070), a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions and Natural Science Foundation of the Higher Education Institutions of Anhui Province (KJ2014A005).

References

  1. J. R. C. Piqueira and V. O. Araujo, “A modified epidemiological model for computer viruses,” Applied Mathematics and Computation, vol. 213, no. 2, pp. 355–360, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  2. L.-X. Yang and X. Yang, “A new epidemic model of computer viruses,” Communications in Nonlinear Science and Numerical Simulation, vol. 19, no. 6, pp. 1935–1944, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  3. C. Gan, X. Yang, W. Liu, Q. Zhu, and X. Zhang, “An epidemic model of computer viruses with vaccination and generalized nonlinear incidence rate,” Applied Mathematics and Computation, vol. 222, pp. 265–274, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  4. B. K. Mishra and S. K. Pandey, “Effect of anti-virus software on infectious nodes in computer network: a mathematical model,” Physics Letters A, vol. 376, no. 35, pp. 2389–2393, 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. B. K. Mishra and S. K. Pandey, “Fuzzy epidemic model for the transmission of worms in computer network,” Nonlinear Analysis: Real World Applications, vol. 11, no. 5, pp. 4335–4341, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  6. B. Yang, “Analysis of SIQR computer virus model with time delay,” Journal of Chongqing Technology Business University (Nature Science Edition), vol. 30, pp. 70–73, 2013. View at Google Scholar
  7. J. G. Ren, X. F. Yang, L. X. Yang, Y. H. Xu, and F. Z. Yang, “A delayed computer virus propagation model and its dynamics,” Chaos, Solitons & Fractals, vol. 45, no. 1, pp. 74–79, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  8. L. Feng, X. Liao, H. Li, and Q. Han, “Hopf bifurcation analysis of a delayed viral infection model in computer networks,” Mathematical and Computer Modelling, vol. 56, no. 7-8, pp. 167–179, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  9. Y. Yao, X.-W. Xie, H. Guo, G. Yu, F.-X. Gao, and X.-J. Tong, “Hopf bifurcation in an Internet worm propagation model with time delay in quarantine,” Mathematical and Computer Modelling, vol. 57, no. 11-12, pp. 2635–2646, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  10. T. Dong, X. Liao, and H. Li, “Stability and Hopf bifurcation in a computer virus model with multistate antivirus,” Abstract and Applied Analysis, vol. 2012, Article ID 841987, 16 pages, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  11. B. K. Mishra and D. K. Saini, “SEIRS epidemic model with delay for transmission of malicious objects in computer network,” Applied Mathematics and Computation, vol. 188, no. 2, pp. 1476–1482, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  12. K. Zhuang and H. Zhu, “Stability and bifurcation analysis for an improved HIV model with time delay and cure rate,” WSEAS Transactions on Mathematics, vol. 12, no. 8, pp. 860–869, 2013. View at Google Scholar · View at Scopus
  13. B. D. Hassard, N. D. Kazarinoff, and Y. H. Wan, Theory and Applications of Hopf Bifurcation, CCambridge University Press, Cambridge, UK, 1981. View at MathSciNet
  14. T. K. Kar and A. Ghorai, “Dynamic behaviour of a delayed predator-prey model with harvesting,” Applied Mathematics and Computation, vol. 217, no. 22, pp. 9085–9104, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus