Modeling the effects of timing parameters on virus propagation

Yang, Wen‐Ching; Wang, Chenxi

doi:10.1145/948187.948198

Cited by 99 publications

(56 citation statements)

References 11 publications

(10 reference statements)

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“…The twofactor model in [7] takes into account the congestion caused by the worm scan packets, as well as the reactive (human) countermeasures that turn infected or susceptible nodes into an immune state. Models that consider the preventive measures (e.g., antivirus and patch management [17]), link bandwidth between systems ( [9], [16], [13]), network topology [18], the slow down caused by automatic treatment and containment measures ( [19], [1], [11], [20]), infection delay and user vigilance [21], have also been proposed in the literature.…”

Section: Related Workmentioning

confidence: 99%

A Novel Stochastic Approach for Modeling Random Scanning Worms

Avlonitis

Magkos

Stefanidakis

et al. 2009

2009 13th Panhellenic Conference on Informatics

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Abstract-Scanning worms grow with different local velocities in different areas, because of non-uniformities that are present in real networks. The present work introduces a new stochastic model elaborating the classical epidemiological model for random scanning strategies. More specifically, random effects in worm spreading velocity are modeled by means of a stochastic differential equation where an explicit expression quantifying randomness is proposed. Furthermore, we explore whether deterministic or stochastic models are appropriate in order to describe the worm propagation phenomenon. To this end we introduce the scale of observation as a crucial parameter. Simulation results are presented validating the proposed analytical results.

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Section: Related Workmentioning

confidence: 99%

A Novel Stochastic Approach for Modeling Random Scanning Worms

Avlonitis

Magkos

Stefanidakis

et al. 2009

2009 13th Panhellenic Conference on Informatics

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“…Currently, there are two main aspects in the study of basic worm propagation models. One is mainly based on the epidemiology model, including Susceptible-Infectious-Susceptible (SIS) model 25 , Kermack-Mckendrick (KM) model 26 , Two-Factor model 27 , et al This model provides a qualitative understanding of worm spread by using nonlinear different equations. In particular, KM model is also named Susceptible-Infectious-Removed (SIR) model, and is widely used as background research of other worm propagation models, SIRS model 28 for example.…”

Section: Worm Propagationmentioning

confidence: 99%

Locator/Identifier Separation: Comparison and Analysis on the Mitigation of Worm Propagation

Wan¹,

Liu²,

Tang³

et al. 2012

IJCIS

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As a basic prerequisite for worm detection based on computational intelligence in networks with locator/identifier separation, it is well worth considering the influence on worm propagation due to the incoming locator/identifier separation. In this paper, according to the characteristics of locator/identifier separation, we systematically analyze the mitigation of worm propagation in three aspects: address semantics, address space and mapping delay. By applying the classical AAWP and SIR worm propagation models, we give a quantitative comparison between today's Internet and networks with locator/identifier separation. In particular, our research results show that, the characteristics of locator/identifier separation can help to markedly mitigate worm propagation, and networks with locator/identifier separation are more resistant to worm propagation than today's Internet.

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“…In [9] Wang and Wang have studied the impact of infection delay on the epidemic threshold and the epidemic steady state for regular graphs. In [9] the infection delay is de…ned as the length of time between the virus arrival at a node and the instant the node becomes infectious.…”

Section: The Impact Of Infection Delaymentioning

confidence: 99%

“…In [9] the infection delay is de…ned as the length of time between the virus arrival at a node and the instant the node becomes infectious.…”

Section: The Impact Of Infection Delaymentioning

confidence: 99%

“…Thus, as soon as a node becomes infected, it becomes infectious and likewise, as soon as a node is cured it is susceptible to re-infection. There are many models that consider more aspects like incubation periods, variable infection rate, a curing process that takes a certain amount of time and so on [3], [6], [9]. In epidemiological theory, many authors refer to an epidemic threshold c, see for instance [3], [1], [6] and [10].…”

Section: Introductionmentioning

confidence: 99%

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Virus spread in complete bi-partite graphs

Omić

Kooij

Mieghem³

2007

2007 2nd Bio-Inspired Models of Network, Information and Computing Systems

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In this paper we study the spread of viruses on the complete bi-partite graph KM;N . Using mean …eld theory we …rst show that the epidemic threshold for this type of graph sati…es c = 1 p M N , hence, con…rming previous results from literature. Next, we …nd an expression for the average number of infected nodes in the steady state. In addition, our model is improved by the introduction of infection delay. We validate our models by means of simulations. Inspired by simulation results, we analyze the probability distribution of the number of infected nodes in the steady state for the case without infection delay. The mathematical model we obtain is able to predict the probability distribution very well, in particular, for large values of the e¤ective spreading rate. It is also shown that the probabilistic analysis and the mean …eld theory predict the same average number of infected nodes in the steady state. Finally, we present a heuristic for the prediction of the extinction probability in the …rst phase of the infection. Simulations show that, for the case without infection delay, this time dependent heuristic is quite accurate.

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Modeling the effects of timing parameters on virus propagation

Cited by 99 publications

References 11 publications

A Novel Stochastic Approach for Modeling Random Scanning Worms

A Novel Stochastic Approach for Modeling Random Scanning Worms

Locator/Identifier Separation: Comparison and Analysis on the Mitigation of Worm Propagation

Virus spread in complete bi-partite graphs

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