- Research
- Open Access
Impact of impulsive detoxication on the spread of computer virus
- Xianxiu Zhang^{1, 2}Email authorView ORCID ID profile,
- Chuandong Li^{1} and
- Tingwen Huang^{3}
https://doi.org/10.1186/s13662-016-0944-x
© Zhang et al. 2016
- Received: 6 June 2016
- Accepted: 16 August 2016
- Published: 25 August 2016
Abstract
We discuss the dynamical properties of a SIRS computer virus propagation model with impulsive detoxication and saturation effect. By the Internet, new antivirus software can be released immediately and take effect quickly after it is running. This leads to the circumstance that many infected computers can be cured in a short time. So impulsive detoxication is a vitally important way for prohibiting the spread of network viruses. The theoretical results show that: (a) the virus-free equilibrium is globally stable when the basic reproduction ratio (BRR) is less than unity, (b) the system is uniformly permanent when BRR exceeds unity, and (c) a supercritical bifurcation occurs when BRR equals unity. Several numerical examples also clearly display the results obtained. Finally, some feasible strategies of eradicating electronic viruses are advised.
Keywords
- computer virus propagation model
- impulsive detoxication
- virus-free equilibrium
- uniformly permanent
- supercritical bifurcation
1 Introduction
While the popularized communication networks have brought great convenience to our daily work and life, they also provide a fast channel for the spread of computer viruses. During the last decades, massive outbreaks of network viruses have caused enormous financial losses.
For the purpose of effectively controlling virus diffusion, it is of practical importance to understand the way that malicious codes propagate over the Internet. Due to the compelling analogy between electronic viruses and their biological counterparts [1, 2], a multitude of computer virus epidemic models, ranging from SIS models [3, 4], SIR models [5, 6], SIRS models [7], SEIR models [8], SEIRS model [9], SLBS models [10–12], SLAS model [13], SIPS model [14], to delayed models [7, 15] and stochastic models [16, 17], have been proposed. In our opinion, these models can help us better understand how the viruses diffuse on networks.
In the classical epidemic models, the incidence rate β, which is the probability of transmission per contact, is assumed to be bilinear with respect to the numbers of susceptible and infected individuals [18]. To the best of our knowledge, in most of previous computer virus models, the incidence rate β is also simply supposed to be unvaried [19, 20]. However, in reality, when the population are aware of the existence of computer viruses on networks, they will reduce the number of communication contacts per unit time to avoid being infected, the more infectious computers being reported, the less contact with other computers, which is called the saturation effect. In an attempt to capture the saturation effect, Yuan et al. [21] proposed a nonlinear incidence rate. As the transmission details of computer infections are quite complicated, their results have only limited applications. Recently, Yang and Yang [22] also suggested a nonlinear incidence rate. However, its description of saturation effect is not clear. Based on a large number of numerical examples, we use \(1/(1+\alpha I(t))\) (α is a positive constant and \(I(t)\) represents the percentage of infected internal computers at time t, respectively) to denote the saturation effect [23], namely, use \(\frac{\beta}{1+\alpha I(t)}\) to replace β. This seems reasonable.
In the prevalence of infectious diseases, it is impossible to rapidly disseminate the new remedy to a great many patients, and it often takes a lot of time to finish one or several courses of the remedy [24–26]. On the contrary, as new antivirus software can be released immediately via the Internet and takes effect quickly after it started running, many of the infected computers can be cured in a short time [20]. To understand how impulsive detoxication [16] and saturation effects prohibit virus spread on networks, a novel impulsive computer virus propagation model is established in this paper. We theoretically analyze that the virus-free equilibrium is globally asymptotically stable when BRR is less than unity. It is shown that the system is uniformly permanent when BRR exceeds unity. In addition, by bifurcation theory we see that a supercritical bifurcation occurs when BRR equals unity. Both theoretical predictions and numerical examples show that impulsive detoxication can control virus diffusion effectively. Finally, considering the influence of varying model parameters on BRR, we propose some feasible strategies of deracinating malicious viruses. One should emphasize that there are few computer virus propagation models in the literature that consider the combined impact of impulsive detoxication and saturation effects.
The remainder of this paper is organized as follows: the new model is elaborated in Section 2; it is theoretically studied in Sections 3, 4, and 5; the influence of model parameters is discussed in Section 6; finally, this work is summarized in Section 7.
2 Model formulation
In this section, we will formulate a computer virus propagation model with impulsive detoxication and saturation effects. As usual, we shall simply assume that every computer is in one of three states: susceptible, infected, and recovered. Both susceptible computers and recovered computers are uninfected, however, the former has no immunity, while the latter has temporary immunity. Let \(S(t)\), \(I(t)\), and \(R(t)\) denote the percentages of susceptible, infected, and recovered internal computers at time t, respectively, then \(S(t)+I(t)+R(t)\equiv1\).
For simplicity, in this paper, we do not consider the situation where the susceptible computers get recovered.
- (H1)
All newly accessed computer are susceptible. Furthermore, susceptible computers are accessed to the Internet at the constant rate \(\mu>0\). And every computer on the Internet leaves the network with constant probability \(\mu>0\).
- (H2)
By contact with infected internal computers, at time t, every susceptible internal computer gets infected with probability \(\frac{\beta I(t)}{1+\alpha I(t)}\), where \(\beta>0\) and \(\alpha>0\) are constants.
- (H3)
By running of previous antivirus software, every infected internal computer gets recovered with constant probability \(\gamma>0\).
- (H4)
Every recovered internal computer loses immunity with constant probability \(\delta>0\).
- (H5)
The latest antivirus software is disseminated at \(t = kT\), \(k \in\mathbb{N}\), where \(\mathbb{N}=\{1,2,3,\ldots\}\), \(T > 0\), is a constant.
- (H6)
For dissemination of the latest antivirus software, a q fraction of infected internal computers get recovered at time instant kT, where \(0< q< 1\), and q is a constant.
3 Virus-free equilibrium and its stability
In this section, we shall prove the existence and global stability of the infection-free equilibrium under certain conditions.
Theorem 1
System (2) has a unique virus-free equilibrium \((0, 0)\).
Clearly, we have the following.
Theorem 2
System (1) has a unique virus-free equilibrium \((1, 0, 0)\).
Theorem 3
If \(\Re_{0}<1\), the virus-free equilibrium \((0, 0)\) of system (2) is locally asymptotically stable.
Proof
To prove the main result of this section, we first show a lemma.
Lemma 1
Proof
Theorem 4
The virus-free equilibrium \((0, 0)\) of system (2) is globally asymptotically stable if \(\Re_{0}<1\).
Proof
Via Theorem 4, we have the following.
Theorem 5
The virus-free equilibrium \((1, 0, 0)\) of system (1) is globally asymptotically stable if \(\Re_{0}<1\).
Example 1
Example 2
From Figures 1 and 2, we can see that the state of system (1) is approaching the virus-free equilibrium, as fits the theoretical prediction. In other words, the computer viruses will be eradicated in these situations.
4 Permanence
In the following, we study uniform permanence of system (2) and system (1). First we give two definitions.
Definition 1
System (2) is said to be virus permanent in Ω, if for every solution \((I (t), R(t))\) to system (2) with \(I (0^{+} ) > 0\), there is \(m > 0\) such that \(I (t)\geq m\) for all large t.
Definition 2
Theorem 6
If \(\Re_{0}>1\), then there exists a positive constant \(m_{3}\), such that any positive solution of system (2) satisfies \(I(t)\geq m_{3}\), for t large enough. That is, system (2) is virus permanent.
Proof
By the above discussions, we have \(I(t)\geq m_{3}\) for all \(t>t_{3}\). The proof is complete. □
From Theorem 6, we get the following.
Corollary 1
System (1) is virus permanent if \(\Re_{0}>1\).
Theorem 7
System (2) is uniformly permanent if \(\Re_{0}>1\).
Proof
As a direct consequence of Corollary 1 and Theorem 7, we have the following.
Corollary 2
If \(\Re_{0}>1\), any positive solution of system (1) satisfies \(I(t)\geq m_{3}\) and \(R(t)\geq m_{4}\), for t large enough.
From the first equation and the fourth equation of system (1) and using Corollary 2, we have \(\frac{dS(t)}{dt} >\mu+ \delta m_{4} - (\mu+ \beta) S(t)\), \(t\geq t_{6}\); furthermore, we get \(\lim_{t\rightarrow\infty}S(t)\geq\frac{\mu+ \delta m_{4}}{\mu+ \beta}\). For a sufficiently small \(\varepsilon_{3}>0\), let \(m_{5}=\frac{\mu+ \delta m_{4}}{\mu+ \beta }-\varepsilon_{3}>0\), there exists a \(t^{\prime}>t_{6}\), such that \(S(t)>m_{5}\) for \(t>t^{\prime}\).
By combining Corollary 2 and the above discussions, we get the following.
Theorem 8
System (1) is uniformly permanent if \(\Re_{0}>1\).
Example 3
Example 4
One can easily find the limit cycle in Figures 6 and 8, respectively.
Based on many numerical examples, we speculate that system (1) has a stable viral periodic solution if \(\Re_{0}>1\).
5 Bifurcation analysis
Now, we investigate the existence of a viral periodic solution by means of the impulsive bifurcation theory in [29]. First, we present it as follows.
Lemma 2
- (a)
If \(BC < 0\), a supercritical bifurcation occurs at \(d_{0} = 0\).
- (b)
If \(BC > 0\), a subcritical bifurcation occurs at \(d_{0} = 0\).
- (c)
If \(BC = 0\), there is an undetermined case.
Theorem 9
For system (2), a supercritical bifurcation occurs at \(\Re_{0}=1\). System (2) has a stable viral periodic solution which bifurcates from the virus-free equilibrium when \(\Re_{0}\) increasingly goes across unity.
Proof
\(\Re_{0}=1\) implies \(d_{0}=0\) and \(\beta-\mu- \gamma>0\), so we get \(B<0\), then \(BC<0\). Clearly, \(0 < a_{0} < 2\). Therefore, the claimed result follows from Lemma 2. □
6 Discussions
By the previous discussions, some practicable and effective strategies should be followed to lower \(\Re_{0}\) below unity for deracinating network viruses. For that purpose, in the following, we study the effects of the changing model parameters on \(\Re_{0}\).
Theorem 10
\(\Re_{0}\) drops with increasing μ, γ and q, and \(\Re_{0}\) rises with increasing β and T.
One can easily see this result from (4).
Remark 1
- Case 1.
\(\beta<\mu+\gamma\). Then \(\Re_{0}<1\) via (4), the computer viruses can also be deracinated without impulsive detoxication.
- Case 2.\(\beta\geq\mu+\gamma\). By (4), we have \(\Re_{0}<1\) if$$ \frac{1}{T}\ln\frac{1}{1-q}>\beta-(\mu+\gamma). $$(25)
Remark 2
We notice that δ and α are not in (4), yet we know that they have significant impact on the spread of computer viruses. It ought to be considered as a demerit of the model, and we shall improve the model in the next research.
- (1)
Strengthening the research of antivirus software and shortening the development cycle are conducive to reduce the impulsive detoxication period T and to enhance the impulsive detoxication rate q and the recovery rate γ.
- (2)
The people must be often reminded to install antivirus software, so that the impulsive detoxication rate q and the recovery rate γ are raised.
- (3)
Let your computer leave the Internet when unnecessary, so that the recruitment rate μ is increased, the incidence rate β is minimized.
7 Conclusions
Taking into account impulsive detoxication and saturation effects in the conventional SIRS model, a novel computer viruses propagation model has been established. The dynamical properties of this model have been investigated theoretically, and the results obtained have also been demonstrated by several numerical examples. Based on an analysis of the impact of varying model parameters on BRR, some effective measures for controlling electronic virus diffusion have been advised.
Recently, Yang et al. [30, 31] analyzed the impact of the structure of the propagation network on the spread of computer virus, and they proposed a node-based epidemic model, which can help us better understand how the electronic viruses diffuse on networks. The final goal is not only to understand epidemic processes and predict their behavior, but also to control their dynamics [32]. Our future work is to research the impact of impulsive detoxication on the spread of computer viruses in different structures of the propagation network.
Declarations
Acknowledgements
This research is supported by the Natural Science Foundation of China (No. 61374078), NPRP grant # NPRP 4-1162-1-181 from the Qatar National Research Fund (a member of Qatar Foundation), Science and technology fund of Guizhou Province (LH [2015] No. 7612), China, and Chongqing Research Program of Basic Research and Frontier Technology (No. cstc2015jcyjBX0052), China.
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.
Authors’ Affiliations
References
- Cohen, F: Computer viruses: theory and experiments. Comput. Secur. 6(1), 22-35 (1987) View ArticleGoogle Scholar
- Murray, WH: The application of epidemiology to computer viruses. Comput. Secur. 7(2), 130-150 (1988) View ArticleGoogle Scholar
- Kephart, JO, White, SR: Directed-graph epidemiological models of computer viruses. In: IEEE Computer Society Symposium on Research in Security and Privacy, pp. 343-359 (1991) Google Scholar
- Kephart, JO, White, SR: Measuring and modeling computer virus prevalence. In: IEEE Computer Society Symposium on Research in Security and Privacy, pp. 2-15 (1993) Google Scholar
- McCluskey, CC: Complete global stability for an SIR epidemic model with delay-distributed or discrete. Nonlinear Anal., Real World Appl. 11, 55-59 (2010) MathSciNetView ArticleMATHGoogle Scholar
- Hattaf, K, Lashari, AA, Louartassi, Y, Yousfi, N: A delayed SIR epidemic model with general incidence rate. Electron. J. Qual. Theory Differ. Equ. 2013, 3 (2013) MathSciNetView ArticleMATHGoogle Scholar
- Muroya, Y, Enatsu, Y, Li, H: Global stability of a delayed SIRS computer virus propagation model. Int. J. Comput. Math. 91(3), 347-367 (2014) MathSciNetView ArticleMATHGoogle Scholar
- Yuan, H, Chen, G: Network virus-epidemic model with the point-to-group information propagation. Appl. Math. Comput. 206(1), 357-367 (2008) MathSciNetMATHGoogle Scholar
- Mishra, BK, Pandey, SK: Dynamic model of worms with vertical transmission in computer network. Appl. Math. Comput. 217(21), 8438-8446 (2011) MathSciNetMATHGoogle Scholar
- Yang, L-X, Yang, X, Zhu, Q, Wen, L: A computer virus model with graded cure rates. Nonlinear Anal., Real World Appl. 14(1), 414-422 (2013) MathSciNetView ArticleMATHGoogle Scholar
- Yang, L-X, Yang, X: A new epidemic model of computer viruses. Commun. Nonlinear Sci. Numer. Simul. 19, 1935-1944 (2014) MathSciNetView ArticleGoogle Scholar
- Chen, L, Hattaf, K, Sun, J: Optimal control of a delayed SLBS computer virus model. Physica A 427, 244-250 (2015) MathSciNetView ArticleGoogle Scholar
- Muroya, Y, Kuniya, T: Global stability of nonresident computer virus models. Math. Methods Appl. Sci. 38, 281-295 (2015) MathSciNetView ArticleMATHGoogle Scholar
- Yang, L-X, Yang, X: A novel virus-patch dynamic model. PLoS ONE (2015). doi:10.1371/journal.pone.0137858 Google Scholar
- Feng, L, Liao, X, Li, H, Han, Q: Hopf bifurcation analysis of a delayed viral infection model in computer networks. Math. Comput. Model. 56(7-8), 167-179 (2012) MathSciNetView ArticleMATHGoogle Scholar
- Yang, X, Yang, L-X: Towards the epidemiological modeling of computer viruses. Discrete Dyn. Nat. Soc. 2012, Article ID 259671 (2012) MATHGoogle Scholar
- Zhang, C, Zhao, Y, Wu, Y, Deng, S: A stochastic dynamic model of computer viruses. Discrete Dyn. Nat. Soc. 2012, Article ID 264874 (2012) MathSciNetMATHGoogle Scholar
- Anderson, RM, May, RM: Infectious Diseases of Humans: Dynamics and Control. Oxford University Press, Oxford (1991) Google Scholar
- Zhang, C, Zhao, Y, Wu, Y: An impulse model for computer viruses. Discrete Dyn. Nat. Soc. 2012, Article ID 260962 (2012) MathSciNetMATHGoogle Scholar
- Yang, L-X, Yang, X: The pulse treatment of computer viruses: a modeling study. Nonlinear Dyn. 76(2), 1379-1393 (2014) MathSciNetView ArticleMATHGoogle Scholar
- Yuan, H, Guan, L, Guan, C: On modeling the crowding and psychological effects in network-virus prevalence with nonlinear epidemic model. Appl. Math. Comput. 219, 2387-2397 (2012) MathSciNetMATHGoogle Scholar
- Yang, L-X, Yang, X: The impact of nonlinear infection rate on the spread of computer virus. Nonlinear Dyn. 82(1), 85-95 (2015) MathSciNetView ArticleGoogle Scholar
- Capasso, V, Serio, G: A generalization of the Kermack-McKendrick deterministic epidemic model. Math. Biosci. 42(1-2), 43-61 (1978) MathSciNetView ArticleMATHGoogle Scholar
- Sabin, A: Measles, killer of millions in developing countries: strategies of elimination and continuing control. Eur. J. Epidemiol. 7(1), 1-22 (1991) MathSciNetGoogle Scholar
- Agur, Z, Cojocaru, L, Mazor, G, Anderson, R, Danon, Y: Pulse mass measles vaccination across age cohorts. Proc. Natl. Acad. Sci. USA 90(24), 11698-11702 (1993) View ArticleGoogle Scholar
- Meng, X, Chen, L, Wu, B: A delay SIR epidemic model with pulse vaccination and incubation times. Nonlinear Anal., Real World Appl. 11(1), 88-98 (2010) MathSciNetView ArticleMATHGoogle Scholar
- Lakshmikantham, V, Bainov, DD, Simeonov, PS: Theory of Impulsive Differential Equations. World Scientific, Singapore (1989) View ArticleMATHGoogle Scholar
- Bainov, DD, Simeonov, PS: Impulsive Differential Equations: Periodic Solutions and Applications. Longman, New York (1993) MATHGoogle Scholar
- Lakmeche, A, Arino, O: Bifurcation of nontrivial periodic solutions of impulsive differential equations arising chemotherapeutic treatment. Dyn. Contin. Discrete Impuls. Syst. 7(2), 265-287 (2000) MathSciNetMATHGoogle Scholar
- Yang, L-X, Draief, M, Yang, X: The impact of the network topology on the viral prevalence: a node-based approach. PLoS ONE (2015). doi:10.1371/journal.pone.0134507 Google Scholar
- Yang, L-X, Draief, M, Yang, X: Heterogeneous virus propagation in networks: a theoretical study. Math. Methods Appl. Sci. (2016). doi:10.1002/mma.4061 Google Scholar
- Romualdo, P-S, Claudio, C, Piet, VM, Alessandro, V: Epidemic processes in complex networks. Rev. Mod. Phys. (2015). doi:10.1103/RevModPhys.87.925 MathSciNetGoogle Scholar