Modeling the state dependent impulse control for computer virus propagation under media coverage

Liang, Xiyin; Pei, Yongzhen; Lv, Yaping

doi:10.1016/j.physa.2017.09.058

Cited by 15 publications

(9 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In response, updating antivirus software and patching operating system are two primary protections. Recently, some researchers described the transmission and prevention of computer worm and virus with the state feedback compulsive model, and they have got some interesting results [7,12,27]. In their models, when the number of infected computers reaches a certain level the impulsive control will be triggered.…”

Section: Meng Zhang Kaiyuan Liu Lansun Chen and Zeyu LImentioning

confidence: 99%

“…Modeling and preliminaries. Enlightened by the development in the field of impulsive control and the progress of infectious disease study [19,29,25,26], all the computers connected with the Internet are divided into two disjoint groups: S(t) and I(t), which also present the number of individuals in each group at time t. The computers in group S are healthy ones and can be infected by the computer worm and virus, and group I is made up of individuals that are infected and they can transform worm and virus to members of S. In the present research [12,27], the incidence form are assumed as the simple item βSI which is contravened with the truth. In fact, the incidence is not remarkably effected by the number of susceptible ones when S occupies a considerable large proportion of the population, especially during the first spreading stage of a certain computer worm or virus.…”

mentioning

confidence: 97%

See 1 more Smart Citation

State feedback impulsive control of computer worm and virus with saturated incidence

Zhang

Liu²,

Chen³

et al. 2018

Mathematical Biosciences &Amp; Engineering

View full text Add to dashboard Cite

A state feedback impulsive model is set up to discuss the spreading and control of the computer worm and virus. Considering the transmission features, saturated infectious is adopted to describe the spreading in the model, and all the treatment measures, such as patching operating system and updating antivirus software, are assumed to take effect instantly. Then the model is analyzed with a novel method, and the existence and stability of order-1 limit cycle are discussed. Finally, the numerical simulation is listed to verify the result of the paper.

show abstract

Section: Meng Zhang Kaiyuan Liu Lansun Chen and Zeyu LImentioning

confidence: 99%

mentioning

confidence: 97%

State feedback impulsive control of computer worm and virus with saturated incidence

Zhang

Liu²,

Chen³

et al. 2018

Mathematical Biosciences &Amp; Engineering

View full text Add to dashboard Cite

show abstract

“…Some mathematical models were applied to depict the spread of computer worms on the Internet due to the similarity of the transmission characteristics of computer worms and infectious diseases. [4][5][6] Kephart and White 7 devised two epidemiological models to explain some phenomena of computer viruses. Wierman and Marchette 8 modified the susceptible-infected-susceptible (SIS) epidemiological model and used it as a computer virus propagation model.…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation of a susceptible–infected–recovered–dead computer worm model

Deng

Pei

2021

SIMULATION

Self Cite

View full text Add to dashboard Cite

Computer worms are serious threats to Internet security and have caused billions of dollars of economic losses during the past decades. In this study, we implemented a susceptible–infected–recovered–dead (SIRD) model of computer worms and analyzed the characteristics and mechanisms of worm transmission. We applied the ordinary differential equation model to simulate the transmission process of computer worms and estimated the unknown parameters of the SIRD model through the methods of least squares, Markov chain Monte Carlo, and ensemble Kalman filtering (ENKF). The results reveal that the proposed SIRD model is more accurate than the susceptible–exposed–infected–recovered–susceptible model with respect to parameter estimation.

show abstract

“…The only limitation here is the researchers' imagination. Complex schemes of compartmental models are usually presented in the form of transition graphs [21][22][23] . The implementation of compartmental models consists of the formalization of transition equations and the subsequent calculation of changes in each compartment during a certain finite period.…”

mentioning

confidence: 99%

Stochastic forecasting of variable small data as a basis for analyzing an early stage of a cyber epidemic

Kovtun,

Grochla,

Kharchenko

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Security Information and Event Management (SIEM) technologies play an important role in the architecture of modern cyber protection tools. One of the main scenarios for the use of SIEM is the detection of attacks on protected information infrastructure. Consorting that ISO 27001, NIST SP 800-61, and NIST SP 800-83 standards objectively do not keep up with the evolution of cyber threats, research aimed at forecasting the development of cyber epidemics is relevant. The article proposes a stochastic concept of describing variable small data on the Shannon entropy basis. The core of the concept is the description of small data by linear differential equations with stochastic characteristic parameters. The practical value of the proposed concept is embodied in the method of forecasting the development of a cyber epidemic at an early stage (in conditions of a lack of empirical information). In the context of the research object, the stochastic characteristic parameters of the model are the generation rate, the death rate, and the independent coefficient of variability of the measurement of the initial parameter of the research object. Analytical expressions for estimating the probability distribution densities of these characteristic parameters are proposed. It is assumed that these stochastic parameters of the model are imposed on the intervals, which allows for manipulation of the nature and type of the corresponding functions of the probability distribution densities. The task of finding optimal functions of the probability distribution densities of the characteristic parameters of the model with maximum entropy is formulated. The proposed method allows for generating sets of trajectories of values of characteristic parameters with optimal functions of the probability distribution densities. The example demonstrates both the flexibility and reliability of the proposed concept and method in comparison with the concepts of forecasting numerical series implemented in the base of Matlab functions.

show abstract

Modeling the state dependent impulse control for computer virus propagation under media coverage

Cited by 15 publications

References 23 publications

State feedback impulsive control of computer worm and virus with saturated incidence

State feedback impulsive control of computer worm and virus with saturated incidence

Parameter estimation of a susceptible–infected–recovered–dead computer worm model

Stochastic forecasting of variable small data as a basis for analyzing an early stage of a cyber epidemic

Contact Info

Product

Resources

About