 Research
 Open access
 Published:
Dynamics of a delayed worm propagation model with quarantine
Advances in Difference Equations volume 2017, Article number: 155 (2017)
Abstract
A delayed SEIQRSV model with quarantine describing the dynamics of worm propagation is considered in the present paper. Local stability of the endemic equilibrium is addressed and the existence of a Hopf bifurcation at the endemic equilibrium is established by analyzing the corresponding characteristic equation. By means of the normal form theory and the center manifold theorem, properties of the Hopf bifurcation at the endemic equilibrium are investigated. Finally, numerical simulations are also given to support our theoretical conclusions.
1 Introduction
A computer worm is a selfcontained program that can spread functional copies of itself or its segments to other systems without depending on another program to host its code [1, 2]. With the development of information technology and the increase of network complexity, the problem of computer worms has become the focus with its tremendous destruction. So, it is of considerable interest to understand the law governing spread of the worms in a network. Enlightened by the fact that propagation of the worms in a network could be compared with infectious diseases in a population, many mathematical models have been established to predict the spread of worms [3–7].
The quarantine strategy is an effective method on controlling disease. Inspired of this, many researchers introduce the quarantine strategy into mathematical models to investigate the spread of the worms in a network [8–10]. In order to describe the dynamics of worm propagation in a network, Kumar et al. proposed the following SEIQRSV model in [10]:
where \(S(t)\), \(E(t)\), \(I(t)\), \(Q(t)\), \(R(t)\) and \(V(t)\) are the numbers of the uninfected computers which have no immunity, the exposed computers which are susceptible to infection, the infected computers which have to be cured, the infected computers which are quarantined, the uninfected computers which have temporary immunity and the vaccinated computers which have susceptibility to infection at time t, respectively. A is the rate at which the new computers are attached to the network; d is the natural death rate of the computers in the network; α is the death rate of computers in the network due to the attack of the worms; β, γ, δ, η, ε, θ, ρ and χ are the state transition rates of system (1).
Clearly, system (1) neglects the delays during the propagation process of the worms in the network. It is well known that delays of one type or another have been incorporated into worm propagation models due to latent period, temporary immunization period or other reasons. Worm propagation models with delay have been investigated by some scholars at home or broad in recent years [11–15]. Delays can play a complicated role in the dynamics of the dynamical models, especially they can cause Hopf bifurcation in the predatorprey models [16–19], epidemic models [20–24] and economic models [25–27]. For worm propagation models, the occurrence of a Hopf bifurcation means that the state of the worm propagation changes from an equilibrium to a limit cycle and this phenomenon makes the propagation of worms out of control. Therefore, it is of substantial importance to investigate the effect of delays on the worm propagation models. Based on this and considering the fact that one of the typical features of worms is its latent characteristic, we incorporate the latent period delay into system (1) and get the following worm propagation model with quarantine strategy and time delay:
where τ is the latent period delay.
The rest of the present paper is organized as follows. In Section 2, we analyze the local stability of the endemic equilibrium and the threshold of a Hopf bifurcation. Section 3 is devoted to the explicit formulas determining direction of the Hopf bifurcation and stability of the bifurcating periodic solutions. In Section 4, a simulation example is presented and the simulation results match well with our obtained theoretical results. Finally, Section 5 draws the conclusions.
2 Analysis of Hopf bifurcation
By a direct computation, we can know that if \(AR_{0}(d+\chi)>d^{2}+(\rho +\chi)d\) and \(\beta(d+\theta)(d+\alpha+\varepsilon)>R_{0}\theta \varepsilon\delta+R_{0}\theta\eta(d+\alpha+\varepsilon)\), then system (2) has a unique endemic equilibrium \(P_{*}(S_{*}, E_{*}, I_{*}, Q_{*}, R_{*}, V_{*})\), where
The linearized system of system (2) at \(P_{*}(S_{*}, E_{*}, I_{*}, Q_{*}, R_{*}, V_{*})\) can be given by
The characteristic equation for system (3) is
where
When \(\tau=0\), equation (4) becomes
with
Obviously, \(D_{1}=r_{05}>0\). Therefore, a set of sufficient conditions for all roots of equation (5) to have a negative real part is given by the RouthHurwitz criteria in the following form:
Assume that \(\lambda=i\omega(\omega>0)\) is a solution of equation (4). Then one can obtain
from which it follows that
where
If all the coefficients of system (2) are given, we can solve equation (12) by Matlab software package easily. So, we make the following assumption.
 \((H_{1})\) :

equation (12) has at least one positive root.
If the condition \((H_{1})\) holds, then there exists \(\omega_{0}>0\) such that equation (4) has a pair of purely imaginary roots \(\pm i\omega_{0}\). For \(\omega_{0}\), one can obtain
where
For equation (4), by direct computation we have
where
Then we obtain
Thus,
with \(f(v)=v^{6}+p_{5}v^{5}+p_{4}v^{4}+p_{3}v^{3}+p_{2}v^{2}+p_{1}v+p_{0}\) and \(v_{*}=\omega_{0}^{2}\).
Therefore, if we have the condition \((H_{2})\): \(f^{\prime}(v_{*})\neq0\), then \(\operatorname {Re}[d\lambda/d\tau]_{\tau=\tau_{0}}^{1}\neq0\). According to the discussion above and the Hopf bifurcation theorem in [28], we can obtain the following.
Theorem 1
For system (2), if the conditions \((H_{1})\)\((H_{2})\) hold, then the endemic equilibrium \(P_{*}(S_{*}, E_{*}, I_{*}, Q_{*}, R_{*}, V_{*})\) is asymptotically stable for \(\tau\in[0, \tau_{0})\); a Hopf bifurcation occurs at the endemic equilibrium \(P_{*}(S_{*}, E_{*}, I_{*}, Q_{*}, R_{*}, V_{*})\) when \(\tau=\tau_{0}\) and a family of periodic solutions bifurcate from the endemic equilibrium \(P_{*}(S_{*}, E_{*}, I_{*}, Q_{*}, R_{*}, V_{*})\) near \(\tau =\tau_{0}\).
3 Direction and stability of the Hopf bifurcation
Motivated by the ideas of Hassard et al. [28], in this section, we will derive the explicit formulas that determine the direction and stability of the Hopf bifurcation at the critical value \(\tau_{0}\). For the sake of simplicity, let \(\tau=\tau_{0}+\mu\), \(\mu\in R\). Then \(\mu=0\) is the Hopf bifurcation value for system (2). Setting \(u_{1}(t)=S(t)S_{*}\), \(u_{2}(t)=E(t)E_{*}\), \(u_{3}(t)=I(t)I_{*}\), \(u_{4}(t)=Q(t)Q_{*}\), \(u_{5}(t)=R(t)R_{*}\), \(u_{6}(t)=V(t)V_{*}\) and \(t\rightarrow(t/\tau)\). Then system (2) can be transformed into functional differential equations in \(C=C([1, 0], R^{6})\):
with
and
Based on the Riesz representation theorem, there exists a bounded variation function \(\eta(\theta, \mu)\) for \(\theta\in[1, 0]\) such that
In fact, we choose
where \(\delta(\theta)\) is the Dirac delta function. Next, for \(\phi\in C\), we define
and
Then system (17) can be transformed into
where \(u_{t}=u(t+\theta)\).
In order to construct the coordinates describing the center manifold near \(\mu=0\), we have to define an inner product and the adjoint operator \(A^{*}\) of \(A(0)\). Letting \(C^{*}=C([0, 1], R^{6})\), for \(\varphi \in C^{*}\), \(A^{*}\) is defined by
where \(\eta^{T}\) is the transpose of η.
For \(\phi\in C\) and \(\varphi\in C^{*}\), an inner product is defined as the following bilinear form:
where \(\eta(\theta)=\eta(\theta, 0)\).
We can see that \(i\omega_{0}\tau_{0}\) is the eigenvalue of \(A(0)\), and \(i\omega_{0}\tau_{0}\) is that of \(A^{*}\). Assume that \(q(\theta)=(1, q_{2}, q_{3}, q_{4}, q_{5}, q_{6})^{T} e^{i\omega_{0}\tau_{0}\theta}\) is the eigenvector of \(A(0)\) corresponding to \(i\omega_{0}\tau_{0}\) and \(q^{*}(s)=M(1, q_{2}^{*}, q_{3}^{*}, q_{4}^{*}, q_{5}^{*}, q_{6}^{*})e^{i\omega_{0}\tau_{0}s}\) is the eigenvector of \(A^{*}\) corresponding to \(i\omega_{0}\tau_{0}\). From the definitions of \(A(0)\) and \(A^{*}\), we can obtain
In addition, from equation (20), one can get the expression of M̄:
such that \(\langle q^{*}, \bar{q}\rangle=1\).
Then, following the procedure in [29–33], we can obtain the expressions of \(g_{20}\), \(g_{11}\), \(g_{02}\) and \(g_{21}\) as follows:
with
and \(E_{1}\) and \(E_{2}\) are given
where
Then we can get the following coefficients:
Thus, the properties of the Hopf bifurcation of system (2) can be stated as follows.
Theorem 2
\(\mu_{2}\) determines the direction of the Hopf bifurcation: if \(\mu _{2}>0\) (\(\mu_{2}<0\)), then the Hopf bifurcation is supercritical (subcritical); \(\rho_{2}\) determines the stability of the bifurcating periodic solutions: if \(\rho_{2}<0\) (\(\rho_{2}>0\)), then the bifurcating periodic solutions are stable (unstable); \(T_{2}\) determines the period of the bifurcating periodic solutions: if \(T_{2}>0\) (\(T_{2}<0\)), then the period of the bifurcating periodic solutions increases (decreases).
4 Numerical simulation
This section is concerned with some numerical simulations of system (2) with the aim of verifying the obtained theoretical results. We choose \(A=2\), \(\beta=0.09\), \(d=0.05\), \(\rho =0.65\), \(\theta=0.05\), \(\chi=0.55\), \(\gamma=0.45\), \(\alpha=0.035\), \(\delta=0.1\), \(\eta=0.35\) and \(\varepsilon=0.07\). Then system (2) becomes
It is easy to verify that \(R_{0}=0.1514\), \(AR_{0}(d+\chi)=0.1817\), \(d^{2}+(\rho+\chi)d=0.0625\), \(\beta(d+\theta)(d+\alpha+\varepsilon )=0.0014\). Therefore, \(AR_{0}(d+\chi)>d^{2}+(\rho+\chi)d\) and \(\beta (d+\theta)(d+\alpha+\varepsilon)>R_{0}\theta\varepsilon\delta +R_{0}\theta\eta(d+\alpha+\varepsilon)\) is satisfied. Then one can obtain the unique endemic equilibrium \(P_{*}(6.6050, 1.1889, 3.2887, 2.1217, 12.9956, 7.1554)\) of system (22). It can be verified that the conditions for the occurrence of a Hopf bifurcation are also satisfied for system (22).
Then, using Matlab 7.0 software package and by some complicated computations, we obtain \(\omega_{0}=0.3703\), \(\tau_{0}=9.7698\), \(\lambda ^{\prime}(\tau_{0})=3.92257.3108i\). We choose \(\tau=9.28<\tau _{0}=9.7698\). Thus, the endemic equilibrium \(P_{*}(6.6050, 1.1889, 3.2887, 2.1217, 12.9956, 7.1554)\) is asymptotically stable when \(\tau<\tau _{0}\), which can be illustrated by computer simulations in Figures 12. When τ passes through the critical value \(\tau_{0}=9.7698\), the endemic equilibrium \(P_{*}(6.6050, 1.1889, 3.2887, 2.1217, 12.9956, 7.1554)\) loses its stability and a Hopf bifurcation occurs, i.e., a family of periodic solutions bifurcate from the endemic equilibrium \(P_{*}(6.6050, 1.1889, 3.2887, 2.1217, 12.9956, 7.1554)\). Choosing \(\tau=10.38>\tau_{0}=9.7698\), the computer simulations are as shown in Figures 34.
Further, we have \(C_{1}(0)=1.96030.6585i\), \(\mu_{2}=0.4998>0\), \(\beta _{2}=3.9206<0\) and \(T_{2}=0.8280>0\). Therefore, according to Theorem 2, the Hopf bifurcation at the critical value \(\tau_{0}=9.7698\) is supercritical; the bifurcating periodic solutions are stable and the period of the bifurcating periodic solutions increases.
5 Conclusions
Based on the fact that one of the significant features of computer viruses is its latent characteristic, we incorporate the latent period delay into the model considered in the literature [10] and obtain the delayed SEIQRSV model describing the worms propagation in a network. Compared with the work in [10], we mainly focus on effects of the delay on the model.
We obtained the sufficient conditions for the local stability of the endemic equilibrium. The stability criteria in the absence of the delay are no longer enough to guarantee the stability in the presence of the delay, rather there is a critical value \(\tau_{0}\) such that the model is stable for \(\tau<\tau_{0}\) and become unstable for \(\tau>\tau_{0}\). By choosing the latent period delay as a bifurcation parameter, and analyzing the corresponding characteristic equation, it is proved that the latent period delay in the model can destabilize the endemic equilibrium and give rise to a Hopf bifurcation. That is, a family of periodic solutions bifurcate from the endemic equilibrium when the delay passes through the critical value. Therefore, we can conclude that when the value of the latent period delay is suitable small, it is helpful to predict and control the propagation of the worms in system (2). Otherwise, the worms persist in the whole host population. For further research, properties of the Hopf bifurcation such as direction and stability are determined by applying the normal theory and the center manifold theorem. Finally, the results are validated by some numerical simulations.
It should be pointed out that many other factors besides time delay can influence a worm propagation system in a real network environment. For example, network congestion and the network topology are also impact factors to worm propagation. Namely, we will link the results obtained with the model proposed in the present paper with the results coming from the networks theory. They will be a major emphasis of our future research.
References
Kumar, M, Mishra, BK, Panda, TC: Predatorprey models on interaction between computer worms, Trojan horse and antivirus software inside a computer system. Int. J. Secur. Appl. 10, 173190 (2016)
Mishra, BK, Pandey, SK: Dynamic model of worms with vertical transmission in computer network. Appl. Math. Comput. 217, 84388446 (2011)
Toutonji, OA, Yoo, SM, Park, M: Stability analysis of veisv propagation modeling for network worm attack. Appl. Math. Model. 36, 27512761 (2012)
Peng, S, Wu, M, Wang, G, et al.: Propagation model of smartphone worms based on semiMarkov process and social relationship graph. Comput. Secur. 44, 92103 (2014)
Mishra, BK, Keshri, N: Mathematical model on the transmission of worms in wireless sensor network. Appl. Math. Model. 37, 41034111 (2013)
Mishra, BK, Pandey, SK: Dynamic model of worm propagation in computer network. Appl. Math. Model. 38, 21732179 (2014)
Mishra, BK, Pandey, SK: Fuzzy epidemic model for the transmission of worms in computer network. Nonlinear Anal., Real World Appl. 11, 43354341 (2010)
Wang, FW, Yang, Y, Zhao, DM, et al.: A worm defending model with partial immunization and its stability analysis. J. Commun. 10, 276283 (2015)
Wang, F, Zhang, Y, Wang, C, et al.: Stability analysis of a SEIQV epidemic model for rapid spreading worms. Comput. Secur. 29, 410418 (2010)
Kumar, M, Mishra, BK, Panda, TC: Effect of quarantine & vaccination on infectious nodes in computer network. Int. J. Comput. Netw. Appl. 2, 9298 (2015)
Yao, Y, Zhang, N, Xiang, WL, et al.: Modeling and analysis of bifurcation in a delayed worm propagation model. J. Appl. Math. 2013, Article ID 927369 (2013)
Wang, C, Chai, S: Hopf bifurcation of an SEIRS epidemic model with delays and vertical transmission in network. Adv. Differ. Equ. 2016, 100 (2016)
Yao, Y, Xiang, WL, Qu, AD, et al.: Hopf bifurcation in an SEIDQV worm propagation model with quarantine strategy. Discrete Dyn. Nat. Soc. 2012, Article ID 304868 (2012)
Yao, Y, Feng, X, Yang, W, et al.: Analysis of a delayed Internet worm propagation model with impulsive quarantine strategy. Math. Probl. Eng. 2014, Article ID 369360 (2014)
Zhang, ZZ, Yang, HZ: Stability and Hopf bifurcation in a delayed SEIRS worm model in computer network. Math. Probl. Eng. 2013, Article ID 319174 (2013)
Liu, J: Bifurcation analysis of a delayed predatorprey system with stage structure and HollingII functional response. Adv. Differ. Equ. 2015, 208 (2015)
Wang, LS, Feng, GH: Global stability of an ecoepidemiological predatorprey model with saturation incidence. J. Appl. Math. Comput. 53, 303319 (2017)
Pal, N, Samanta, S, Biswas, S, et al.: Stability and bifurcation analysis of a threespecies food chain model with delay. Int. J. Bifurc. Chaos 25, 1550123 (2015)
Biswas, S, Sasmal, SK, Samanta, S, et al.: Optimal harvesting and complex dynamics in a delayed ecoepidemiological model with weak Allee effects. Nonlinear Dyn. 87, 15531573 (2017)
Liu, J, Wang, K: Hopf bifurcation of a delayed SIQR epidemic model with constant input and nonlinear incidence rate. Adv. Differ. Equ. 2016, 168 (2016)
Wang, WY, Chen, LJ: Stability and Hopf bifurcation analysis of an epidemic model by using the method of multiple scales. Math. Probl. Eng. 2016, Article ID 2034136 (2016)
Sun, XG, Wei, JJ: Stability and bifurcation analysis in a viral infection model with delays. Adv. Differ. Equ. 2015, 332 (2015)
Wang, TL, Hu, ZX, Liao, FC: Stability and Hopf bifurcation for a virus infection model with delayed humoral immunity response. J. Math. Anal. Appl. 411, 6374 (2014)
Liu, J: Bifurcation of a delayed SEIS epidemic model with a changing delitescence and nonlinear incidence rate. Discrete Dyn. Nat. Soc. 2017, Article ID 2340549 (2017)
Gori, L, Guerrini, L, Sodini, M: Hopf bifurcation in a cobweb model with discrete time delays. Discrete Dyn. Nat. Soc. 2014, Article ID 137090 (2014)
Gori, L, Guerrini, L, Sodini, M: Hopf bifurcation and stability crossing curves in a cobweb model with heterogeneous producers and time delays. Nonlinear Anal. Hybrid Syst. 18, 117133 (2015)
Liao, MX, Xu, CJ, Tang, XH: Stability and Hopf bifurcation for a competition and cooperation model of two enterprises with delay. Commun. Nonlinear Sci. Numer. Simul. 19, 38453856 (2014)
Hassard, BD, Kazarinoff, ND, Wan, YH: Theory and Applications of Hopf Bifurcation. Cambridge University Press, Cambridge (1981)
Bianca, C, Ferrara, M, Guerrini, L: The time delays’ effects on the qualitative behavior of an economic growth model. Abstr. Appl. Anal. 2013, Article ID 901014 (2013)
Bianca, C, Ferrara, M, Guerrini, L: The Cai model with time delay: existence of periodic solutions and asymptotic analysis. Appl. Math. Inf. Sci. 7, 2127 (2013)
Meng, XY, Huo, HF, Zhang, XB, et al.: Stability and Hopf bifurcation in a threespecies system with feedback delays. Nonlinear Dyn. 64, 349364 (2011)
Jana, D, Agrawal, R, Upadhyay, RK: Toppredator interference and gestation delay as determinants of the dynamics of a realistic model food chain. Chaos Solitons Fractals 69, 5063 (2014)
Ferrara, M, Guerrini, L, Bisci, GM: Center manifold reduction and perturbation method in a delayed model with a moundshaped CobbDouglas production function. Abstr. Appl. Anal. 2013, Article ID 738460 (2013)
Acknowledgements
This research was supported by Natural Science Foundation of Anhui Province (Nos. 1608085QF145, 1608085QF151, 1708085MA17) and Natural Science Foundation of the Higher Education Institutions of Anhui Province (No. KJ2015A144).
Author information
Authors and Affiliations
Corresponding author
Additional information
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
All authors contributed equally to the writing of this paper. All authors read and approved the final manuscript.
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
Zhang, Z., Song, L. Dynamics of a delayed worm propagation model with quarantine. Adv Differ Equ 2017, 155 (2017). https://doi.org/10.1186/s1366201712124
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s1366201712124