A SEIR Model for Computer Virus Spreading Based on Cellular Automata

Batista, Farrah Kristel; Rey, Ángel Martín del; Quintero-Bonilla, Santiago; Dios, Araceli Queiruga

doi:10.1007/978-3-319-67180-2_62

Cited by 8 publications

(9 citation statements)

References 13 publications

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“…Related to this, Artalejo and Lopez-Herrero 18 discusses the dynamics of susceptible-infective-removed (SIR) model with the aid of continuous time Markov chains. A cellular automata based SEIR model for computer virus spreading is proposed in Batista et al 19 A stochastic approach of analysis covering SIR models is considered in Kenah and Robins. 20 Several important characteristics are common to almost all fields of research, such as mathematical biology, computer science, physics, economics, and the socail science.…”

Section: Motivations and Related Workmentioning

confidence: 99%

Building epidemic models for living populations and computer networks

Kondakci¹,

Kondakci²

2021

Science Progress

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Accurate modeling of viral outbreaks in living populations and computer networks is a prominent research field. Many researchers are in search for simple and realistic models to manage preventive resources and implement effective measures against hazardous circumstances. The ongoing Covid-19 pandemic has revealed the fact about deficiencies in health resource planning of some countries having relatively high case count and death toll. A unique epidemic model incorporating stochastic processes and queuing theory is presented, which was evaluated by computer simulation using pre-processed data obtained from an urban clinic providing family health services. Covid-19 data from a local corona-center was used as the initial model parameters (e.g. [Formula: see text], infection rate, local population size, number of contacts with infected individuals, and recovery rate). A long–run trend analysis for 1 year was simulated. The results fit well to the current case data of the sample corona center. Effective preventive and reactive resource planning basically depends on accurately designed models, tools, and techniques needed for the prediction of feature threats, risks, and mitigation costs. In order to sufficiently analyze the transmission and recovery dynamics of epidemics it is important to choose concise mathematical models. Hence, a unique stochastic modeling approach tied to queueing theory and computer simulation has been chosen. The methods used here can also serve as a guidance for accurate modeling and classification of stages (or compartments) of epidemics in general.

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Section: Motivations and Related Workmentioning

confidence: 99%

Building epidemic models for living populations and computer networks

Kondakci¹,

Kondakci²

2021

Science Progress

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“…The states of the model were susceptible, infected, recovered, damaged, and out-of-order. Batista et al [ 39 ] presented an SEIR model to simulate the spread of computer viruses on a computer network. In this model, the parameters considered are related to the life cycle of the computer virus, the countermeasures implemented on the host, and the user’s behavior.…”

Section: Related Workmentioning

confidence: 99%

A Novel Multi-Agent Model for Robustness with Component Failure and Malware Propagation in Wireless Sensor Networks

Zhang

et al. 2021

Sensors

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A wireless sensor network (WSN) is a group of sensors connected with a wireless communications infrastructure designed to monitor and send collected data to the primary server. The WSN is the cornerstone of the Internet of Things (IoT) and Industry 4.0. Robustness is an essential characteristic of WSN that enables reliable functionalities to end customers. However, existing approaches primarily focus on component reliability and malware propagation, while the robustness and security of cascading failures between the physical domain and the information domain are usually ignored. This paper proposes a cross-domain agent-based model to analyze the connectivity robustness of a system in the malware propagation process. The agent characteristics and transition rules are also described in detail. To verify the practicality of the model, three scenarios based on different network topologies are proposed. Finally, the robustness of the scenarios and the topologies are discussed.

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“…A SEIR model to simulate the propagation of computer virus through a computer network was introduced in Reference [27]. The mathematical tool used was cellular automata on graphs.…”

Section: Related Workmentioning

confidence: 99%

A New Individual-Based Model to Simulate Malware Propagation in Wireless Sensor Networks

2020

Self Cite

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Wireless Sensor Networks (WSNs) are a set of sensor devices deployed in a given area that form a network without a pre-established architecture. Recently, malware has increased as a potential vulnerability for the Internet of Things, and consequently for these networks. The spread of malware on wireless sensor networks has been studied from different perspectives, excluding individual characteristics in most of the models proposed. The primary goal of this work is to introduce an Agent-Based Model for analysing malware propagation on these networks, and its agents, coefficients and transition rules are detailed. Finally, some simulations of the proposed model are included.

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A SEIR Model for Computer Virus Spreading Based on Cellular Automata

Cited by 8 publications

References 13 publications

Building epidemic models for living populations and computer networks

Building epidemic models for living populations and computer networks

A Novel Multi-Agent Model for Robustness with Component Failure and Malware Propagation in Wireless Sensor Networks

A New Individual-Based Model to Simulate Malware Propagation in Wireless Sensor Networks

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