Modeling Malware Propagation in Wireless Sensor Networks with Individual-Based Models

Rey, Ángel Martín del; Guillén, Jose Diamantino Hernández; Sánchez, Gerardo Rodríguez

doi:10.1007/978-3-319-44636-3_18

Cited by 8 publications

(9 citation statements)

References 14 publications

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“…Del Rey et al [ 38 ] proposed an improved individual-based model, which used characteristics of three types of nodes and complex topology. The states of the model were susceptible, infected, recovered, damaged, and out-of-order.…”

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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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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“…The authors of Reference [19] proposed an improved individual-based model that use particular features of three types of nodes and complex topology. The compartments of the model are susceptible, infected, recovered, damaged, and out-of-order.…”

Section: Related Workmentioning

confidence: 99%

“…In previously proposed models, system characteristics such as the different types of sensor nodes, the malware and, in some recent, cases the topology have been considered as actors [19]. The SEIRS-D agent-based model proposed in this study takes into account other essential characteristics of these actors and new actors that have still not considered.…”

Section: Introductionmentioning

confidence: 99%

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

2020

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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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“…Due to distributed structure and weak defence mechanism malware attack in WSN is easy. Malware attack on WSN leads to the various threats such as authentication of information, availability of services, confidentiality of information, and energy exhaustion of sensor nodes [9]. Malware attacker launches the attack by targeting WSN and executes intentional malicious process on sensor node of WSN and makes them infectious.…”

Section: Introductionmentioning

confidence: 99%

An epidemic model for the investigation of multi‐malware attack in wireless sensor network

Awasthi¹,

Srivastava²,

Kumar³

et al. 2023

IET Communications

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The protection of wireless sensor networks (WSN) against malware attacks is crucial. The paper discusses the issue of malware attacks in WSN, which are commonly used for monitoring and surveillance in various applications. Due to resource constraints, sensor nodes in WSN are vulnerable to malware attacks, which can spread rapidly and paralyze the network. The development of new technologies such as IoT, Industry 4.0 has increased the importance of WSN, and it has become essential to address the challenges posed by the resource‐constrained nature of sensor nodes and security concerns. In this paper, a model is considered with two exposed states to investigate the behaviour of malware spreading in WSN, and a SE1E2IR (Susceptible—Exposed State 1 ‐ Exposed State 2 ‐ Infectious—Recovered) model is proposed. The model is formulated as a system of differential equations, and its equilibrium and stability are examined. The basic reproduction number (R0) is also calculated as a key parameter that characterizes the spread of malware in the network. This parameter helps to identify the conditions under which the network will remain malware‐free or when it will experience an outbreak of malware. The proposed model provides a mechanism for the earlier detection of malware occurrences in WSN, and also discusses the effect of connectivity and coverages on the propagation of malware in the network. The paper also includes a comparative study of the proposed model with existing models; extensive theoretical study and computation analysis are performed to validate the proposed model.

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Modeling Malware Propagation in Wireless Sensor Networks with Individual-Based Models

Cited by 8 publications

References 14 publications

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

An epidemic model for the investigation of multi‐malware attack in wireless sensor network

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