- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Recently Accepted Articles ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents

Discrete Dynamics in Nature and Society

Volume 2012 (2012), Article ID 260962, 13 pages

http://dx.doi.org/10.1155/2012/260962

## An Impulse Model for Computer Viruses

School of Information Engineering, Guangdong Medical College, Dongguan 523808, China

Received 9 May 2012; Accepted 4 June 2012

Academic Editor: Xiaofan Yang

Copyright © 2012 Chunming Zhang 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.

#### Abstract

Computer virus spread model concerning impulsive control strategy is proposed and analyzed. We prove that there exists a globally attractive infection-free periodic solution when the vaccination rate is larger than . Moreover, we show that the system is uniformly persistent if the vaccination rate is less than . Some numerical simulations are finally given to illustrate the main results.

#### 1. Introduction

Computer virus is a kind of computer program that can replicate itself and spread from one computer to others. Viruses mainly attack the file system and worms use system vulnerability to search and attack computers. As hardware and software technology develop and computer networks become an essential tool for daily life, the computer virus starts to be a major threat. Consequently, the trial on better understanding of the computer virus propagation dynamics is an important matter for improving the safety and reliability in computer systems and networks. Similar to the biological virus, there are two ways to study this problem: microscopic and macroscopic models. Following a macroscopic approach, since [1, 2] took the first step towards modeling the spread behavior of computer virus, much effort has been done in the area of developing a mathematical model for the computer virus propagation [3–13]. These models provide a reasonable qualitative understanding of the conditions under which viruses spread much faster than others.

In [4], the authors investigated a differential model by making the following assumptions. (H1)The total population of computers is divided into three groups: susceptible, infected, and recovered computers. Let , and denote the numbers of susceptible, infected and recovered computers, respectively. (H2)New computers are attached to the computer network with constant rate . For the sake of antivirus software, some new nodes have temporary immunity with probability , some have not with probability . Hence, new nodes are added into the susceptible class with rate and into the recovered class with rate . (H3)Computers are disconnected to the computer network with the constant rate and are disconnected from the attack of malicious object with probability . (H4) computers become with constant rate and with constant time delay ; computers become with constant rate and with constant time delay ; computers become with constant rate .

According to the above assumptions, the following model (see Figure 1) is derived:

As we know, antivirus software is a kind of computer program which can detect and eliminate known viruses. There are two common methods that an antivirus software application uses to detect viruses: using a list of virus signature definitions and using a heuristic algorithm to find viruses based on common behaviors. It has been observed that it does not always work in detecting a novel computer virus by using the heuristic algorithm. On the other hand, obviously, it is impossible for antivirus software to find new computer viruss signature definitions on the dated list. So, to keep the antivirus soft in high efficiency, it is important to ensure that it is updated. Based on the above facts, we propose an impulsive system to model the process of periodic installing or updating antivirus software on susceptible computers at fixed time for controlling the spread of computer virus.

Based on above facts, we propose the following assumptions.(H5) The antivirus software is installed or updated at time , where is the period of the impulsive effect.(H6) computers are successfully vaccinated from class to class with rate ().

According to the above assumptions (H1)–(H6), and for the reason of simplicity we propose the following model with one time delay (see Figure 2):

The total population size can be determined by to form the differential equation which is derived by adding the equations in system (1.1). Thus the total population size may vary in time. From (1.2), we have It follows that .

Before going into any details, we simplify model (1.1) and restrict our attention to the following model: The initial conditions for (1.5) are From physical considerations, we discuss system (1.5) in the closed set where denotes the nonnegative cone of including its lower-dimensional faces. Note that it is positively invariant with respect to (1.7).

The organization of this paper is as follows. In Section 2, we first state three lemmas which are essential to our proofs and establish sufficient condition for the global attractivity of infection-free periodic solution. The sufficient condition for the permanence of the model is obtained in Section 3. Some numerical simulations are performed in Section 4. In the final section, a brief conclusion is given and some future research directions are also pointed out.

#### 2. Global Attractivity of Infection-Free Periodic Solution

In this section, we prove that the infection-free periodic solution is globally attractive under some conditions. To prove the main results, two lemmas (given in [14]) which are essential to the proofs are stated here.

Lemma 2.1 (see [14], Lemma 1). *Consider the following impulsive system:
**
where , , . Then there exists a unique positive periodic solution of system (2.1)
**
which is globally asymptotically stable, where .*

Lemma 2.2 (see [14], Lemma 2). *Consider the following linear neutral delay equation:
**
If or , then increasing does not change the stability of (2.3).*

When , (2.3) becomes

Corollary 2.3. *Consider system (2.4) and assume that for . Then we have the following statements: *(i)*assume that , then ; *(ii)*assume that , then . **From the third and sixth equations of system (1.5), we have . Further, if , we have the following limit system
**
From the second and fourth equations of system (3.5), we have and have the following limit systems of (3.5):
**
According to Lemma 2.1, we know that the periodic solution of system (3.10) is of the form
**
and it is globally asymptotically stable, where .*

Theorem 2.4. *The infection-free periodic solution of system (1.5) is globally attractive provided that , where
*

*Proof. *Since , we can choose sufficiently small such that
It follows from the third equation of system (1.5) that
There exists an integer such that .

From the first equation of system (1.5), we have
For , we consider the following comparison differential system:
In view of Lemma 2.1, we know that the unique periodic solution of system (2.12) is of the form
and it is globally asymptotically stable. From (1.5), we have
From (2.9), we have that . According to Corollary 2.3 we have
Therefore, for any (sufficiently small), there exists an integer such that for all . From the third equation of system (1.5), we have
Consider the comparison equation
It is easy to see that . It follows by the comparison theorem that there exists an integer such that
Since is arbitrarily small, from and (2.18) we have
It follows from (2.15) and (2.19) that there exists such that
Hence, from the first equation of system (1.5) we have that
Consider the following comparison impulsive differential equations for and ,
In view of Lemma 2.1, we periodic solution of system
which is globally asymptotically stable, where
According to the comparison theorem for impulsive differential equation, there exists an integer such that
Because is arbitrarily small, it follows from (2.25) that
is globally attractive, that is,
It follows from (2.15), (2.19), (2.27), and the restriction that . Hence, the infection-free periodic solution of system (1.5) is globally attractive. The proof is completed.

Corollary 2.5. *The infection-free periodic solution of system (1.5) is globally attractive, if where .*

Theorem 2.4 determines the global attractivity of (1.5) in for the case . Its realistic implication is that the infected computers vanish so the computer virus removed from the network. Corollary 2.5 implies that the computer virus will disappear if the vaccination rate is larger than .

#### 3. Permanence

In this section, we say the computer virus is local if the infectious population persists above a certain positive level for sufficiently large time. The locality viruses can be well captured and studied through the notion of permanence.

*Definition 3.1. * System (1.5) is said to be uniformly persistent if there is an (independent of the initial data) such that every solution with initial conditions (1.7) of system (1.5) satisfies

*Definition 3.2. *System (1.5) is said to be permanent if there exists a compact region such that every solution of system (1.5) with initial data (1.7) will eventually enter and remain in region . Denote

Theorem 3.3. *Suppose that . Then there is a positive constant such that each positive solution of system (1.5) satisfies , for large enough. *

*Proof. *Now, we will prove there exist and a sufficiently large such that holds for all . Suppose that for all . From the first equation of (1.5), we have

Consider the following comparison system:
By Lemma 2.1, we know that, there exists such that
It follows from the second equation of (1.5) that . Consider the comparison system . Noting that and is sufficiently small, we have .

Corollary 2.3 implies that , . This contradicts . Hence, we can claim that, for any , it is impossible that
By the claim, we are left to consider two cases. First, for large enough. Second, oscillates about for large enough. Obviously, there is nothing to prove for the first case. For the second case, we can choose and satisfy
is uniformly continuous since the positive solutions to (1.5) are ultimately bounded and is not effected by impulses.

Therefore, it is certain that there exists (, and is independent of the choice of ) such that
In this case, we shall consider the following three possible cases in term of the sizes of , and . *Case *1. If , then it is obvious that , for . *Case *2. If , then from the second equation of system (1.5), we obtain . Since , it is obvious that , for . *Case *3. If , it is easy to obtain that for . Then, proceeding exactly the proof for above claim, we have that for .

Owing to the randomicity of , we obtain that there exists , such that holds for all . The proof of Theorem 3.3 is completed.

Theorem 3.4. *Suppose . Then system (1.5) is permanent. *

*Proof. * Let be any solution to system (1.5). First, from the first equation of system (1.5), we have . Consider the following comparison system:

By Lemmas 2.1 and 2.2, we know that for any sufficiently small , there exists ( is sufficiently large) such that
From the third equation of (1.5), we have . It is easy to see that . Let . By Theorem 3.3 and above discussions, we know that the set is a global attractor in , and of course, every solution of system (1.5) with initial conditions (1.7) will eventually enter and remain in region . Therefore, system (1.5) is permanent. The proof is completed.

Corollary 3.5. *It follows from Theorem 3.4 that the system (1.5) is uniformly persistent provided that , where .*

#### 4. Numerical Simulations

In this section, we perform some numerical simulations to show the geometric impression of our results. To demonstrate the global attractivity of infection-free periodic solution to system (1.5), we take the following parameter values: , and . In this case, we have . In Figures 3(a), 3(b), and 3(c) display respectively the susceptible, infected and recovered population of system (1.5) with initial conditions: and . Figure 3(d) shows their corresponding phase-portrait.

To demonstrate the permanence of system (1.5) we take following set parameter values: , and . In this case, we have . In Figures 4(a), 4(b), and 4(c) display, respectively, the susceptible, infected and recovered population of system (1.5) with initial conditions: , and . Figure 4(d) shows their corresponding phase portrait.

#### 5. Conclusion

We have analyzed the delayed model with pulse vaccination and varying total population size. We have shown that or implies that the disease will be endemic, whereas or implies that the disease will fade out. We have also established sufficient condition for the permanence of the model. Our results indicate that a short interpulse time or a large pulse vaccination rate will lead to eradication of the computer virus.

In this paper, we have only discussed two cases: (i) (or ) and (ii) (or ). But for closed interval [, ] (or [,]), the dynamical behavior of model (3) have not been studied, and the threshold parameter for the reproducing number (or the pulse vaccination rate) between the extinction of the computer viruses and the uniform persistence of the viruses have not been obtained. These issues would be left to our future consideration.

#### Acknowledgment

This paper is supported by the National Natural Science Foundation of China (no. 61170320), the Natural Science Foundation of Guangdong Province (no. S2011040002981) and the Scientific research Foundation of Guangdong Medical College (no. KY1048).

#### References

- J. O. Kephart and S. R. White, “Directed-graph epidemiological models of computer viruses,” in
*Proceedings of the IEEE Symposium on Security and Privacy*, pp. 343–359, 1991. View at Scopus - J. O. Kephart, S. R. White, and D. M. Chess, “Computers and epidemiology,”
*IEEE Spectrum*, vol. 30, no. 5, pp. 20–26, 1993. - L. Billings, W. M. Spears, and I. B. Schwartz, “A unified prediction of computer virus spread in connected networks,”
*Physics Letters A*, vol. 297, no. 3-4, pp. 261–266, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH - X. Han and Q. Tan, “Dynamical behavior of computer virus on Internet,”
*Applied Mathematics and Computation*, vol. 217, no. 6, pp. 2520–2526, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH - B. K. Mishra and N. Jha, “Fixed period of temporary immunity after run of anti-malicious software on computer nodes,”
*Applied Mathematics and Computation*, vol. 190, no. 2, pp. 1207–1212, 2007. View at Publisher · View at Google Scholar · View at Scopus - B. K. Mishra and D. Saini, “Mathematical models on computer viruses,”
*Applied Mathematics and Computation*, vol. 187, no. 2, pp. 929–936, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH - B. K. Mishra and S. K. Pandey, “Dynamic model of worms with vertical transmission in computer network,”
*Applied Mathematics and Computation*, vol. 217, no. 21, pp. 8438–8446, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH - 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 Zentralblatt MATH - J. R. C. Piqueira, “Dynamic models for computer viruses,”
*Computers and Security*, vol. 27, no. 7-8, pp. 355–359, 2008. View at Scopus - J. Ren, X. Yang, L.-X. Yang, Y. Xu, and F. 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 - J. Ren, X. Yang, Q. Zhu, L.-X. Yang, and C. Zhang, “A novel computer virus model and its dynamics,”
*Nonlinear Analysis*, vol. 13, no. 1, pp. 376–384, 2012. View at Publisher · View at Google Scholar - J. C. Wierman and D. J. Marchette, “Modeling computer virus prevalence with a susceptible-infected-susceptible model with reintroduction,”
*Computational Statistics & Data Analysis*, vol. 45, no. 1, pp. 3–23, 2004. View at Publisher · View at Google Scholar - H. Yuan and G. Chen, “Network virus-epidemic model with the point-to-group information propagation,”
*Applied Mathematics and Computation*, vol. 206, no. 1, pp. 357–367, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH - J. Ren, X. Yang, Q. Zhu, L.-X. Yang, and C. Zhang, “A novel computer virus model and its dynamics,”
*Nonlinear Analysis*, vol. 13, no. 1, pp. 376–384, 2012. View at Publisher · View at Google Scholar · View at Scopus