# Stability and Hopf Bifurcation of a Delayed Epidemic Model of Computer Virus with Impact of Antivirus Software.

1. IntroductionComputer viruses are programs created to carry out activities in a computer without consent of its owner. They not only disrupt the normal functionalities of computer system and damage data files in the computer, but also cause heavy economic losses and tremendous social impacts [1-3]. In recent years, mathematical modeling enjoys popularity in both analyzing and controlling computer viruses based on the similarity between computer viruses and biological viruses. A few works proposing SIR models have appeared in the literatures [4-6]. In [4], Amador studied a stochastic SIRA epidemic model for computer viruses and analyzed the quasistationary distribution, the extinction time, and the number of infections in order to understand the spreading mechanism of computer viruses. In [5], Ozturk and Gulsu proposed an approximate solution to a modified SIR computer virus model by using the shifted Chebyshev collocation method. In [6], Khanh studied the stability and approximate iterative solutions of an SIR computer virus model with antidotal component.

Considering the latent period of computer viruses, some models with latency are proposed by some scholars [1,7-10]. In [7], Yang investigated global stability of a VEISV network worm attack model by using the Li-Muldowney geometric approach. In [8], Keshri et al. proposed a reduced SEIR scale-free network model and studied its stability. In [9 ], Hosseini et al. formulated a discrete-time SEIRS model of computer virus propagation in scale-free networks and analyzed the local and global stability of the model. In [1], Guillen et al. proposed an improved SEIRS worm model with considering accurate positions for dysfunctional hosts and their replacements in state transition. In [10], Ren and Xu investigated an SEIR-KS computer virus propagation model based on the kill signals. They studied the local and global stability of the model by applying Routh-Hurwitz criterion and Lyapunov functional method. There are also some other models considering the latency of computer viruses with quarantine [11-14] and vaccination [15-19].

However, as stated in [20], those above models with the exposed compartment neglect the fact that a computer can infect other computers through file copying or file downloading. Therefore, to overcome the above-mentioned defect, computer virus models with infectivity in latent period have received much attention in recent years [21-26]. Unfortunately, most of these models still have some defects. On the one hand, they ignore the effect of time delay. As is known, there are some time delays of one type or another in the transmission process of computer viruses due to latent period, temporary immunity period, or other reasons. On the other hand, only the susceptible computers are regarded as the entering computers, but every computer can enter or leave the Internet easily in reality. Finally, they neglect the effect of antivirus software, especially the effect of antivirus software on the susceptible computers. Based on the discussion above, we investigated a delayed SLBRS computer virus model with impact of antivirus software based on the following model proposed in [27]:

[mathematical expression not reproducible], (1)

where S(t), L(t), B(t), and R(t) denote the numbers of susceptible, latent, breaking, and recovered computers at time t, respectively. More parameters are listed in Table 1 as follows.

Considering the temporary immune period of the recovered computers, we incorporate the time delay due to the temporary immunity period into system (1) and obtain the following delayed model:

[mathematical expression not reproducible], (2)

where [tau] is the time delay due to the temporary immunity period.

The remainder of the paper is structured as follows. In Section 2, conditions for local stability of the endemic equilibrium and the existence of Hopf bifurcation are performed. Section 3 deals with global stability of the endemic equilibrium. Section 4 is devoted to establishing the formulae to determine the direction, stability, and period of the bifurcating periodic solutions. Some numerical simulations are presented to illustrate the theoretical results in Section 5. We end the paper with a brief conclusion in Section 6.

2. Local Stability and Existence of Hopf Bifurcation

By a direct computation, it can be concluded that system (2) has the endemic equilibrium [E.sub.*] ([S.sub.*], [L.sub.*], [B.sub.*], [R.sub.*]) where

[mathematical expression not reproducible] (3)

where [L.sub.*] is the positive root of (4)

[mathematical expression not reproducible], (4)

where

[mathematical expression not reproducible], (5)

and

[mathematical expression not reproducible]. (6)

The Jacobian matrix of system (2) evaluated at [E.sub.*] is

[mathematical expression not reproducible], (7)

where

[mathematical expression not reproducible]. (8)

The corresponding characteristic equations is

[mathematical expression not reproducible], (9)

with

[mathematical expression not reproducible]. (10)

For [tau] = 0, (9) becomes

[[lambda].sup.4] + ([P.sub.3] + [Q.sub.3]) [[lambda].sup.3] + ([P.sub.2] + [Q.sub.2]) [[lambda].sup.2] + ([P.sub.1] + [Q.sub.1]) [lambda] + [P.sub.0] + [Q.sub.0] = 0. (11)

Clearly, [P.sub.3] + [Q.sub.3] = 3[[gamma].sub.1] + 4[[mu].sub.0] + 2[[gamma].sub.2] + [alpha] + [eta] + [beta]([L.sub.*] + [B.sub.*]) > 0. Hence, it follows from the Hurwitz criterion that all the roots of (11) have negative real parts, if [mathematical expression not reproducible] holds.

For [tau] > 0, let [lambda] = i[omega]([omega] > 0) be the root of (9). Then,

[mathematical expression not reproducible]. (12)

Thus,

[mathematical expression not reproducible], (13)

with

[mathematical expression not reproducible]. (14)

Let [[omega].sup.2] = v, then (13) becomes

[mathematical expression not reproducible]. (15)

Suppose that ([H.sub.2]) (15) has a positive root [v.sub.0]. Then, (13) has a positive root [[omega].sub.0] = [square root of [v.sub.0] such that (9) has a pair of purely imaginary roots [+ or -]i[[omega].sub.0]. For [[omega].sub.0],

[[tau].sub.0] = 1/[[omega].sub.0] x arccos {[F.sub.1]([[omega].sub.0])/[F.sub.2]([[omega].sub.0])}, (16)

where

[mathematical expression not reproducible]. (17)

Differentiating on both sides of (9) with respect to [tau], one can obtain

[mathematical expression not reproducible] (18)

Further, we have

[mathematical expression not reproducible], (19)

where [mathematical expression not reproducible].

Therefore, if ([H.sub.3]): f'([v.sub.0]) [not equal to] 0, then [mathematical expression not reproducible]. Thus, we have the following results based the Hopf bifurcation theorem in [28].

Theorem 1. For system (2), if ([H.sub.1])-([H.sub.3]) hold, then [E.sub.*] ([S.sub.*], [L.sub.*], [B.sub.*], [R.sub.*]) is locally asymptotically stable when [tau] [epsilon] [0, [[tau].sub.0]); system (2) undergoes a Hopf bifurcation at [E.sub.*] ([S.sub.*], [L.sub.*], [B.sub.*], [R.sub.*]) when [tau] = [[tau].sub.0] and a family of periodic solutions bifurcate from [E.sub.*]([S.sub.*], [L.sub.*], [B.sub.*], [R.sub.*]). [[tau].sub.0] is defined as in (16).

3. Global Stability Analysis

Theorem 2. If min{[l.sub.1], [l.sub.2], [l.sub.3], [l.sub.4]} > 0, with

[mathematical expression not reproducible], (20)

where [m.sub.1] < S(t) < [M.sub.1], [m.sub.2] < L(t) < [M.sub.2], [m.sub.3] < B(t) < [M.sub.3], and [m.sub.4] < R(t) < [M.sub.4] for t >0, then the endemic equilibrium [E.sub.*] is globally asymptotically stable.

Proof. Let

[mathematical expression not reproducible]. (21)

Then [E.sub.*],([S.sub.*], [L.sub.*], [B.sub.*], [R.sub.*]) becomes the trivial equilibrium w(t) = x(t) = y(t) = z(t) = 0 for all t >0, and system (2) can be reduced to the following form:

[mathematical expression not reproducible] (22)

[mathematical expression not reproducible] (23)

dy/dt = [alpha][L.sub.*]/B ([e.sup.x(t)] - 1) - [1/B] ([b.sub.3] + [alpha][L.sub.*]) ([e.sup.y(t)] - 1), (24)

[mathematical expression not reproducible]. (25)

Now, we have

[mathematical expression not reproducible]. (26)

Now, (22) can be rewritten as follows by using above relation,

[mathematical expression not reproducible], (27)

Let [V.sub.1](t) = [absolute value of w(t)]. It follows from the above equation

[mathematical expression not reproducible], (28)

We find that there exists a [t.sub.1] >0, such that [R.sup.*][e.sup.z(t)] < [M.sub.4] for all t > [t.sub.1] and for t > [t.sub.1] + [tau], we have

[mathematical expression not reproducible] (29)

Again due to form of (29) we consider the following functional:

[mathematical expression not reproducible], (30)

whose derivative along the solution of system (2) is given by

D[mathematical expression not reproducible] (31)

Again let [V.sub.2](t) = [absolute value of x(t)] and [V.sub.3](t) = [absolute value of y(t)]. Now calculate the derivative of [V.sub.2](t) and V3(t) with the solution of (2), it follows from, respectively, (23) and (24)

[mathematical expression not reproducible], (32)

[D.sup.+][V.sub.3] [less than or equal to] [alpha][L.sub.*]/[m.sub.3] [absolute value of ([e.sup.x(t) - 1)] [1/[M.sub.3] ([b.sub.3] + [alpha][L.sub.*]]) [absolute value of ([e.sup.y(t) - 1)]], (33)

Now, (25) can be rewritten as follows by using (26),

[mathematical expression not reproducible]. (34)

Again let [V.sub.4](t) = [absolute value of z(i)]. It follows from the above equation

[mathematical expression not reproducible]. (35)

We find that there exists a [t.sub.1] > 0, such that [R.sup.*][e.sup.z(t)] < [M.sub.4] for all t > [t.sub.1] and for t > [t.sub.1] + [tau], we have

[mathematical expression not reproducible]. (36)

Again due to the above form of (36) we consider the following functional:

[mathematical expression not reproducible], (37)

whose right derivative along the solution of the system (2) is given by

[mathematical expression not reproducible] (38)

Let us define a Lyapunov functional V(t) as

V(t) = [V.sub.11] (t)+[V.sub.2] (t)+[V.sub.3] + [V.sub.44] (t) > [absolute value of w(t)] + [absolute value of x(t)] + [absolute value of y(t)] + [absolute value of z(t)]. (39)

Computing the upper right derivative of V(t) along the solution of system (2) and by using (31)-(33) and (38), we obtain

[mathematical expression not reproducible], (40)

where [l.sub.1], [l.sub.2], [l.sub.3], and [l.sub.4] are defined above in (20).

Since the model system (2) is positive invariant, therefore, for all t > [t.sup.*.sub.1], we have

[mathematical expression not reproducible]. (41)

Using the mean value theorem, we have

[mathematical expression not reproducible], (42)

where [mathematical expression not reproducible] lies between [S.sub.*] and S(t), [mathematical expression not reproducible] lies between [L.sub.*] and L(t), [mathematical expression not reproducible] lies between [B.sub.*] and B(t), and [mathematical expression not reproducible] lies between [R.sub.*] and R(t). Therefore,

[mathematical expression not reproducible], (43)

where l = min {[l.sub.1][S.bar], [l.sub.2][L.bar], [l.sub.3][B.bar], [l.sub.4][R.bar]}.

Note that V(t) > [absolute value of w(t)] + [absolute value of x(t)] + [absolute value of y(t)] + [absolute value of z(t)]. Hence, from theory of global stability and (43), we conclude that the zero solution of the reduced system (22)-(25) is globally asymptotically stable. Therefore, the endemic equilibrium [E.sup.*] of model system (2) is globally asymptotically stable.

4. Direction and Stability of Hopf Bifurcation

Let [tau] = [[tau].sub.0] + [zeta] ([zeta] [member of] R), [u.sub.1] = S([tau]t), [u.sub.2] = L([tau]t), [u.sub.3] = B([tau]t), and [u.sub.4] = R([tau]t). System (2) becomes

[??](t) = [L.sub.[zeta]] ([u.sub.t]) + F([zeta],[u.sub.t]), (44)

where u(t) = [([u.sub.1], [u.sub.2], [u.sub.3], [u.sub.4]).sup.T] [member of] C = C([-1,0],[R.sup.4]) and [L.sub,[zeta]]: C [right arrow] [R.sup.4] and F: R x C [right arrow] [R.sup.4] are defined as follows:

[L.sub.[zeta]][phi] = ([[tau].sub.0] + [zeta]) ([P.sub.max][phi](0) + [Q.sub.max][phi] (-1)), (45)

and

[mathematical expression not reproducible] (46)

with

[mathematical expression not reproducible], (47)

Thus, there exists [eta]([theta], [zeta]) such that

[L.sub.[zeta]][phi] = [[integral].sup.0.sub.-1] d[eta]([theta], [zeta]) [phi]([theta]), for [phi] [member of] C. (48)

In fact,

[eta] ([theta],[zeta]) = ([[tau].sub.0] + [zeta]) ([P.sub.max][delta] ([theta]) + [Q.sub.max][delta] ([theta]+1)), (49)

where [delta]([theta]) is the Dirac delta function.

For [phi] [member of] C([-1,0], [R.sup.4]), define

[mathematical expression not reproducible], (50)

and

[mathematical expression not reproducible]. (51)

Then system (44) is equivalent to

[??](t) = A([zeta])[u.sub.t] + R([zeta])[u.sub.t]. (52)

For [phi] [member of] [C.sup.1] ([0,1], [([R.sup.4]).sup.*]), define

[mathematical expression not reproducible] (53)

and the bilinear inner form for A and [A.sup.*]

[mathematical expression not reproducible], (54)

where [eta]([theta]) = [eta]([theta],0).

Let [mathematical expression not reproducible] be the eigenvector of A(0) corresponding to + i[[tau].sub.0][[omega].sub.0] and [mathematical expression not reproducible] be the eigenvector of [A.sup.*] (0) corresponding to -i[[tau].sub.0][[omega].sub.0], respectively. Based on the definition of A(0) and [A.sup.*], we obtain

[mathematical expression not reproducible]. (55)

From (54), the expression of Q can be obtain as follows:

[mathematical expression not reproducible], (56)

such that ([v.sup.*], v) = 1 and ([v.sup.*], [bar.v]) = 0.

Next, we can obtain the expressions of [g.sub.20], [g.sub.11], [g.sub.02], and [g.sub.21] by the algorithms in [28] and the computation process in [29-31]:

[mathematical expression not reproducible], (57)

with

[mathematical expression not reproducible]. (58)

[E.sub.1] and [E.sub.2] can be obtained by the following two equations:

[mathematical expression not reproducible]. (59)

Then, one can obtain

[mathematical expression not reproducible], (60)

In conclusion, we have the following results.

Theorem 3. For system (2), if [[mu].sub.2] > 0 ([[mu].sub.2] < 0), then the Hopf bifurcation is supercritical (subcritical); if [[beta].sub.2] < 0 ([[beta].sub.2] > 0),then the bifurcating periodic solutions are stable (unstable); if [T.sub.2] > 0 ([T.sub.2] <0), then the period of the bifurcating periodic solutions increases (decrease).

5. Numerical Simulations

In this section, we develop some numerical simulations in order to support the obtained results in our paper. A set of parameters of system (2) are chosen as follows: [b.sub.1] = 1, [b.sub.2] = 1, [b.sub.3] = 2, [b.sub.4] = 1, [alpha] = 0.35, [beta] = 0.03, [[gamma].sub.1] = 0.1, [[gamma].sub.2] = 0.3, [eta] = 0.5, and [[mu].sub.0] = 0.01. Then, we obtain the following specific case of system (2):

[mathematical expression not reproducible], (61)

Then, (4) becomes the following form:

-0.0113[L.sup.2] +2.40341+ 6.7325 = 0, (62)

from which we can obtain the unique positive root [L.sub.*] = 215.4556. Further, we can verify that system (61) has a unique endemic equilibrium [E.sub.*] (13.4193,215.4556, 188.8036,83.6066) and all the conditions given in Theorem 1 are satisfied.

By means of Matlab software, we get [[omega].sub.0] = 2.0684, [[tau].sub.0] = 3.4685, and [lambda]'([[tau].sub.0]) = 0.0081 + 1.0307i. Thus, we can obtain [C.sub.1](0) = -0.0560 + 0.0092i, [[mu].sub.2] = 6.9136, [[beta].sub.2] = -0.1120, and [T.sub.2] = -0.9945. It follows that [[mu].sub.2] > 0, [[beta].sub.2] < 0, and [T.sub.2] > 0. Fix [tau] = 3.2575 < [[tau].sub.0], then we can see that the solution of system (61) would tend to the endemic equilibrium [E.sub.*] (13.4193, 215.4556, 188.8036, 83.6066). In other words, [E.sub.*] (13.4193, 215.4556, 188.8036, 83.6066) is locally asymptotically stable, which can be illustrated by Figures 1-3. However, when [tau] passes through the critical value [[tau].sub.0], [E.sub.*](13.4193,215.4556, 188.8036,83.6066) loses its stability, and a Hopf bifurcation occurs and a family of periodic solutions bifurcate from [E.sub.*](13.4193,215.4556, 188.8036,83.6066). This property can be shown as in Figures 4-6. Since [[mu].sub.2] > 0, [[beta].sub.2] < 0, and [T.sub.2] < 0, we can conclude that the Hopf bifurcation occurring at [[tau[.sub.0] = 3.4685 is supercritical and the bifurcating periodic solutions are stable and decrease. Next, we are interested to study the effect of some other parameters on the dynamics of system (62).

(i) Effect of the recovered rate ([[gamma].sub.1]): in Figures 7(a)-7(d), we can see that the numbers of susceptible and recovered computers increase; nevertheless, the numbers of latent and breaking computers decrease, when the number of [[gamma].sub.1] increases. And the system changes its behavior from limit cycle to stable focus as we increase the value of [[gamma].sub.1], from 0.1 to 0.3, which can be shown as in Figure 8.

(ii) Effect of the rate of latent and breaking computers reinstall the operating system ([[gamma].sub.2]): in the same manner, we can see from Figures 9(a)-9(d) that the numbers of susceptible and latent computers increase and the number of breaking computers decreases, when the number of [[gamma].sub.2] increases. But it does not affect the number of recovered computers, which can be also seen from the expression of [R.sub.*] in Section 2. Also, we observe that [[gamma].sub.2] does not affect the dynamics of the system; it remains at limit cycle when we choose [tau] = 3.6755. This property can be illustrated by Figure 10.

(iii) Effect of the entering rates ([b.sub.1], [b.sub.2], [b.sub.3], [b.sub.4]): as is shown in Figures 11-14, the numbers of all computers increase when the numbers of [b.sub.1] and [b.sub.4] increase. However, the number of susceptible computers decreases and the numbers of latent, breaking, and recovered computers increase, when the numbers of [b.sub.2] and [b.sub.3] increase. In addition, we find that the entering rates does not affect the dynamics of the systems.

6. Conclusions

In this paper, a delayed SLBRS computer virus model is presented by incorporating the time delay due to the temporary immunity period of the recovered computers based on the model proposed in [27]. Compared with the model in [27], we mainly consider the effect of the time delay on its dynamic behavior. Compared with other computer virus models, we assume that every computer can enter the Internet, which is consistent with the reality. Further, we also consider the effect of antivirus software on the susceptible computers in the presented model. Thus, the computer virus model proposed in our paper is more general.

It has been shown that the endemic equilibrium [E.sub.*]([S.sub.*], [L.sub.*], [R.sub.*]) is locally asymptotically stable when [tau] [member of] [0,[[tau].sub.0]) under some certain conditions. In this case, the propagation of the computer virus in system (2) can be controlled easily. Once the value of the time delay passes through [[tau].sub.0], [E.sub.*]([S.sub.*],[L.sub.*], [B.sub.*], [R.sub.*]) loses its stability and a Hopf bifurcation occurs and a family of periodic solutions bifurcate from [E.sub.*]([S.sub.*], [L.sub.*], [B.sub.*], [R.sub.*]). In this case, the numbers of the four classes computers in system (2) will oscillate in the vicinity of [E.sub.*]([S.sub.*], [L.sub.*], [B.sub.*], [R.sub.*]). Namely, the propagation of the computer virus will be out of control. Therefore, the results obtained in the present paper can help us to gain insight into the spreading process of computer viruses. Also, sufficient conditions for global stability of the endemic equilibrium are derived by constructing a suitable Lyapunov function. Furthermore, properties of the Hopf bifurcation are investigated by using the normal form theory and center manifold theorem. Numerical simulations are presented to verify the analytical predictions. In addition, it has been observed in our simulations that the recovered rate [[gamma].sub.1] can change the dynamics of the system from limit cycle to stable focus as its value increases. Thus, it is strongly recommended that users of computers connected to Internet should periodically run antivirus software of the newest version. From the point of this view, we can conclude that the results of the proposed model in our paper can be used to evaluate the effectiveness of antivirus software. In addition, the numbers of latent and breaking computers decrease, when the reinstalling of the operating system rate increases. Thus, it can be concluded that users should reinstall operating system if necessary. Finally, the numbers of latent and breaking computer will also increase, when the values of entering rates of all computers [b.sub.1], [b.sub.2], [b.sub.3], and [b.sub.4] increase. Therefore, the manager of a network should control the number of computers connected to the network properly.

Of course, when we pursue a low level of infections, we should also consider the cost of the measures we carry out. In addition, it should be pointed out that the model investigated in the literature [27] and our present paper assumes that the latent computers and the breaking computers have the same infection rate [beta]. In the near future, we will investigate the optimal control problem of the following general system (63) so as to achieve a low level of infections at a low cost by using the method introduced in [32]:

[mathematical expression not reproducible], (63)

where [[beta].sub.1] and [[beta].sub.2] are the infection rate of the latent computers and the breaking computers, respectively.

https://doi.org/10.1155/2018/8239823

Data Availability

The data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This research was supported by National Natural Science Foundation of China (no. 11461024), Natural Science Foundation of Inner Mongolia Autonomous Region (no. 2018MS01023), Natural Science Foundation of Anhui Province (nos. 1608085QF145, 1608085QF151, and 1708085MA17), Project of Support Program for Excellent Youth Talent in Colleges and Universities of Anhui Province (nos. gxyqZD2018044 and gxbjZD49), and Bengbu University National Research Fund Cultivation Project (2017GJPY03).

References

[1] J. D. H. Guillen, A. M. del Rey, and L. H. Encinas, "Study of the stability of a SEIRS model for computer worm propagation," Physica A: Statistical Mechanics and its Applications, vol. 479, pp. 411-421, 2017.

[2] U. Fatima, M. Ali, N. Ahmed, and M. Rafiq, "Numerical modeling of susceptible latent breaking-out quarantine computer virus epidemic dynamics," Heliyon, vol. 4, no. 5, p. e00631, 2018.

[3] F. Wang, W. Huang, Y. Shen, and C. Wang, "Analysis of SVEIR worm attack model with saturated incidence and partial immunization," Journal of Communications and Information Networks, vol. 1, no. 4, pp. 105-115, 2016.

[4] J. Amador, "The stochastic SIRA model for computer viruses," Applied Mathematics and Computation, vol. 232, pp. 1112-1124, 2014.

[5] Y. Ozturk and M. Gulsu, "Numerical solution of a modified epidemiological model for computer viruses," Applied Mathematical Modelling, vol. 39, no. 23-24, pp. 7600-7610, 2015.

[6] N. Huu, "Dynamical analysis and approximate iterative solutions of an antidotal computer virus model," International Journal of Applied and Computational Mathematics, vol. 3, no. suppl. 1, pp. S829-S841, 2017.

[7] Y. Yang, "Global stability of VEISV propagation modeling for network worm attack," Applied Mathematical Modelling: Simulation and Computation for Engineering and Environmental Systems, vol. 39, no. 2, pp. 776-780, 2015.

[8] N. Keshri, A. Gupta, and B. K. Mishra, "Impact of reduced scale free network on wireless sensor network," Physica A: Statistical Mechanics and its Applications, vol. 463, pp. 236-245, 2016.

[9] S. Hosseini, M. A. Azgomi, and A. T. Rahmani, "Malware propagation modeling considering software diversity and immunization," Journal of Computational Science, vol. 13, pp. 49-67, 2016.

[10] J. Ren and Y. Xu, "A compartmental model for computer virus propagation with kill signals," Physica A: Statistical Mechanics and its Applications, vol. 486, pp. 446-454, 2017.

[11] X. Xiao, P. Fu, C. Dou, Q. Li, G. Hu, and S. Xia, "Design and analysis of SEIQR worm propagation model in mobile internet," Communications in Nonlinear Science and Numerical Simulation, vol. 43, pp. 341-350, 2017.

[12] R. P. Ojha, G. Sanyal, P. K. Srivastava, and K. Sharma, "Design and analysis of modified SIQRS model for performance study of wireless sensor network," Scalable Computing: Practice and Experience, vol. 18, no. 3, pp. 229-241, 2017.

[13] Q. Badshah, "Global stability of SEIQRS computer virus propagation model with non-linear incidence function," Applied Mathematics, vol. 06, no. 11, pp. 1926-1938, 2015.

[14] C. H. Nwokoye, G. C. Ozoegwu, and V. E. Ejiofor, "Prequarantine approach for defense against propagation of malicious objects in networks," International Journal of Computer Network and Information Security, vol. 9, no. 2, pp. 43-52, 2017.

[15] B. K. Mishra and N. Keshri, "Mathematical model on the transmission of worms in wireless sensor network," Applied Mathematical Modelling, vol. 37, no. 6, pp. 4103-4111, 2013.

[16] R. K. Upadhyay, S. Kumari, and A. K. Misra, "Modeling the virus dynamics in computer network with SVEIR model and nonlinear incident rate," Applied Mathematics and Computation, vol. 54, no. 1-2, pp. 485-509, 2017.

[17] R. K. Upadhyay and S. Kumari, "Detecting malicious chaotic signals in wireless sensor network," Physica A: Statistical Mechanics and its Applications, vol. 492, pp. 1129-1152, 2018.

[18] C. H. Nwokoye and I. I. Umeh, "The SEIQR-V model: on a more accurate analytical characterization of malicious threat defense," International Journal of Information Technology and Computer Science, vol. 9, no. 12, pp. 28-37, 2017.

[19] A. Singh, A. K. Awasthi, K. Singh, and P. K. Srivastava, "Modeling and analysis of worm propagation in wireless sensor networks," Wireless Personal Communications, vol. 98, pp. 2535-2551, 2018.

[20] 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.

[21] T. Zhao and D. Bi, "Hopf bifurcation analysis for an epidemic model over the internet with two delays," Advances in Difference Equations, Paper No. 97,19 pages, 2018.

[22] M. Kumar, B. K. Mishra, and T. C. Panda, "Stability analysis of a quarantined epidemic model with latent and breaking-out over the internet," International Journal of Hybrid Information Technology, vol. 8, no. 7, pp. 133-148, 2015.

[23] C. Tang and Y. Wu, "Global exponential stability of nonresident computer virus models," Nonlinear Analysis: Real World Applications, vol. 34, pp. 149-158, 2017.

[24] Z. Zhang and D. Bi, "Dynamical analysis of a computer virus propagation model with delay and infectivity in latent period," Discrete Dynamics in Nature and Society, vol. 2016, Article ID 3067872, 9 pages, 2016.

[25] Y. Muroya, H. X. Li, and T. Kuniya, "On global stability of a nonresident computer virus model," Acta Mathematica Scientia, vol. 34, no. 5, pp. 1427-1445, 2014.

[26] L. Yang, X. Yang, Q. Zhu, and L. Wen, "A computer virus model with graded cure rates," Nonlinear Analysis: Real World Applications, vol. 14, no. 1, pp. 414-422, 2013.

[27] X. Yang, B. Liu, and C. Gan, "Global stability of an epidemic model of computer virus," Abstract and Applied Analysis, vol. 2014, Article ID 456320,5 pages, 2014.

[28] B. D. Hassard, N. D. Kazarinoff, and Y.-H. Wan, Theory and Applications of Hopf Bifurcation, Cambridge University Press, 1981.

[29] C. Xu, "Delay-induced oscillations in a competitor-competitor-mutualist lotka-volterra model," Complexity, vol. 2017, Article ID 2578043, 12 pages, 2017.

[30] R. K. Upadhyay and R. Agrawal, "Dynamics and responses of a predator-prey system with competitive interference and time delay," Nonlinear Dynamics, vol. 83, pp. 821-837, 2016.

[31] L. Gori, L. Guerrini, and M. Sodini, "Hopf bifurcation in a cobweb model with discrete time delays," Discrete Dynamics in Nature and Society, vol. 2014, Article ID 137090, 8 pages, 2014.

[32] L.-X. Yang, M. Draief, and X. Yang, "The optimal dynamic immunization under a controlled heterogeneous node-based SIRS model," Physica A: Statistical Mechanics and its Applications, vol. 450, pp. 403-415, 2016.

Zizhen Zhang (iD), (1) Ranjit Kumar Upadhyay, (2) Dianjie Bi, (1) and Ruibin Wei (1)

(1) School of Management Science and Engineering, Anhui University of Finance and Economics, Bengbu 233030, China

(2) Department of Applied Mathematics, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India

Correspondence should be addressed to Zizhen Zhang; zzzhaida@163.com

Received 2 September 2018; Revised 8 October 2018; Accepted 18 October 2018; Published 4 November 2018

Academic Editor: Seenith Sivasundaram

Caption: Figure 1: Time plots of S, L, 5, and R with [tau] = 3.2575 < [[tau].sub.0] = 3.4685.

Caption: Figure 2: Dynamic behavior of system (61): projection on S-L-B with [tau] = 3.2575 < [[tau].sub.0] = 3.4685.

Caption: Figure 3: Dynamic behavior of system (61): projection on L-B-R with [tau] = 3.2575 < [[tau].sub.0] = 3.4685.

Caption: Figure 4: Time plots of S, L, B, and R with [tau] = 3.6755 > [[tau].sub.0] = 3.4685.

Caption: Figure 5: Dynamic behavior of system (61): projection on S-L-B with [tau] = 3.6755 > [[tau].sub.0] = 3.4685.

Caption: Figure 6: Dynamic behavior of system (61): projection on L-B-R with [tau] = 3.6755 > [[tau].sub.0] = 3.4685.

Caption: Figure 7: Time plots of S, L, B, and R for different [[gamma].sub.1] at [tau] = 3.2575. Rest of the parameters are taken as given in the text.

Caption: Figure 8: Dynamic behavior of system (61): projection on L-B-R with [tau] = 3.6755 > [[tau].sub.0] = 3.4685 for different [[gamma].sub.1]. Rest of the parameters are taken as given in the text.

Caption: Figure 9: Time plots of S, L, B, and R for different [[gamma].sub.2] at [tau] = 3.2575. Rest of the parameters are taken as given in the text.

Caption: Figure 10: Dynamic behavior of system (61): projection on L-B-R with [tau] = 3.6755 > [[tau].sub.0] = 3.4685 for different [[gamma].sub.2]. Rest of the parameters are taken as given in the text.

Caption: Figure 11: Time plots of S, L, B, and R for different [b.sub.1] at [tau] = 3.2575. Rest of the parameters are taken as given in the text.

Caption: Figure 12: Time plots of S, L, B, and R for different [b.sub.2] at [tau] = 3.2575. Rest of the parameters are taken as given in the text.

Caption: Figure 13: Time plots of S, L, B, and R for different [b.sub.3] at [tau] = 3.2575. Rest of the parameters are taken as given in the text.

Caption: Figure 14: Time plots of S, L, B, and R for different [b.sub.3] at [tau] = 3.2575. Rest of the parameters are taken as given in the text.

Table 1: Parameters and their meanings in this paper. Parameter Description [b.sub.1] Entering rate of susceptible computers [b.sub.2] Entering rate of latent computers [b.sub.3] Entering rate of breaking computers [b.sub.4] Entering rate of recovered computers [alpha] Rate of latent computers break [beta] Infection rate of susceptible computers [[gamma].sub.1] Recovery rate of all computers [[mu].sub.0] Leaving rate of all computers [eta] Rate of recovered computers lose immunity [[gamma].sub.2] Rate of latent and breaking computers reinstall the operating system

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Title Annotation: | Research Article |
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Author: | Zhang, Zizhen; Upadhyay, Ranjit Kumar; Bi, Dianjie; Wei, Ruibin |

Publication: | Discrete Dynamics in Nature and Society |

Date: | Jan 1, 2018 |

Words: | 5231 |

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