Optimal Control of Malware Spreading Model with Tracing and Patching in Wireless Sensor Networks

Muthukrishnan, Senthilkumar; Muthukumar, S; Veeramani, Chinnadurai

doi:10.1007/s11277-020-07959-y

Cited by 27 publications

(12 citation statements)

References 30 publications

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“…Liu et al [38] presented an SILS model to disclose the epidemic process in wireless rechargeable sensor networks, which consists of states Susceptible, Infectious, Low-energy, and Susceptible. Muthukrishnan et al [39] gave a WSNs nodebased epidemic SITPS model including states Susceptible, Infectious, Traced, Patched, and Susceptible. Other typical models consist of an SEIRS-V [40] model introducing V (Vaccination) into the SEIR model, an SEIRS-V model considering the factor of software diversity [41], a VCQPS model reflecting both the heterogeneity and mobility of SNs [42], an epidemic SEIQRV model aggregating quarantine and vaccination techniques [43], a general SEIR model with vaccination-based sliding control [44], an SIC model reflecting countermeasure and network topology [45], an SIQVD model based on time delay and changeable infection probability [46], an SIR-based containment model with mobile social IoT [47], an SEIRD model with Cellular Automaton [48], as well as an SEIRS-V model considering the impact of mobile devices [49].…”

Section: Related Workmentioning

confidence: 99%

SIR₁R₂: Characterizing Malware Propagation in WSNs With Second Immunization

2021

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As an infrastructure of Internet of Things, WSNs (Wireless Sensor Networks) play a more and more important role. However, WSNs are vulnerable to malware and malware can destroy their data security and integrity, which motivates us to explore the role of malware propagation in WSNs. First, according to the actual propagation characteristics of malware in the WSNs and the process of the density change of all node types, we propose an epidemiology-based malware propagation model in consideration of a secondary immune mechanism. Then we set up differential equations to describe the propagation model. By solving differential equations, we can obtain two kinds of equilibrium points indicating that the density of all node types tends to be stable in the WSNs. One is to achieve an equilibrium where only susceptible nodes exist in the WSNs. The other is that malware always exists in the WSNs. Moreover, we prove the local and global stability of these two equilibrium points. Eventually, we analyze the influence and effect of the secondary immunity, forgetting mechanism and containment mechanism on malware propagation in the WSNs, and validate the proposed model through simulations.

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Section: Related Workmentioning

confidence: 99%

SIR₁R₂: Characterizing Malware Propagation in WSNs With Second Immunization

2021

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“…The concept of optimal control theory has very useful in epidemiology, for controlling the transmission of disease. References 20,21 and 22 discussed the node‐based epidemic models based on optimal control theory. Okuonghae and Omame 23 have analyzed the control measures and how they affect the disease's dynamic.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear analysis and dynamics of COVID‐19 mathematical model with optimal control strategies

Muthukumar

Myilsamy

Balakumar

et al. 2023

Optim Control Appl Methods

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Efficacy of the healthcare system and illumination (awareness) activities control COVID‐19. To defend public health, the spreading pandemic of COVID‐19 disease necessitates social distancing, wearing masks, personal cleanliness, and precautions. Due to inadequate awareness programs, COVID‐19 rapidly increases in India. The primary goal of this research is to investigate the spreading behavior of the COVID‐19 virus in India when people are aware of the disease. We find the optimum value of disease transmission rate and detection of the unidentified asymptomatic and symptomatic populations. An optimal control problem is designed with limited resource allocation to improve the recovered individuals. A stability analysis presents for emphasizes the relevance of disease awareness in preventing the spread of the disease. The control parameters are used to explore the increase and decrease of the infected individual with and without control in optimal control analysis. The model is simulated using the Hattaf‐fractional derivative to study the memory effect in the epidemic. To adapt the model to the total number of reported COVID‐19 cases in India, we collected data from March 20, 2021 to September 30, 2021. According to the simulation results, the pandemic would spread faster if awareness campaigns were improperly carried out.

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“…COVID-19-infected people can have symptoms as symptomatic or not have symptoms as asymptomatic, but both can spread the disease [4] . [7] , [8] discussed the node based epidemic models. Population-based models are investigated by [9] , [10] .…”

Section: Introductionmentioning

confidence: 99%

“… [21] developed an optimal control of the classical epidemic SIR model by including vaccination as the control measure. [7] , [8] discussed the node-based epidemic models based on optimal control theory. Okuonghae and Omame [22] have analyzed the control measures and how they affect the disease’s dynamic.…”

Section: Introductionmentioning

confidence: 99%

An optimal control of bi-modal COVID-19 SEIQR epidemic spreading model in India

Muthukumar,

Balakumar,

Ravikumar

et al. 2023

Results in Control and Optimization

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Optimal Control of Malware Spreading Model with Tracing and Patching in Wireless Sensor Networks

Cited by 27 publications

References 30 publications

SIR₁R₂: Characterizing Malware Propagation in WSNs With Second Immunization

SIR₁R₂: Characterizing Malware Propagation in WSNs With Second Immunization

Nonlinear analysis and dynamics of COVID‐19 mathematical model with optimal control strategies

An optimal control of bi-modal COVID-19 SEIQR epidemic spreading model in India

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Optimal Control of Malware Spreading Model with Tracing and Patching in Wireless Sensor Networks

Cited by 27 publications

References 30 publications

SIR1R2: Characterizing Malware Propagation in WSNs With Second Immunization

SIR1R2: Characterizing Malware Propagation in WSNs With Second Immunization

Nonlinear analysis and dynamics of COVID‐19 mathematical model with optimal control strategies

An optimal control of bi-modal COVID-19 SEIQR epidemic spreading model in India

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SIR₁R₂: Characterizing Malware Propagation in WSNs With Second Immunization

SIR₁R₂: Characterizing Malware Propagation in WSNs With Second Immunization