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Cited by 61Security in Wireless Sensor NetworksMobile Ad Hoc NetworksNetwork Security and Intrusion Detection

The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.

An extensive validation of a SIR epidemic model to study the propagation of jamming attacks against IoT wireless networks

Open access
López, Miguel, Peinado, Alberto, Ortiz, Andrés·2019·Computer Networks
DOI 10.1016/j.comnet.2019.106945
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Abstract

Abstract This paper describes the utilization of an epidemic approach to study the propagation of jamming attacks, which can affect to different communication layers of all nodes in a variety of Internet of Things (IoT) wireless networks, regardless of the complexity and computing power of the devices. The jamming term considers both the more classical approach of interfering signals focusing on the physical level of the systems, and the cybersecurity approach that includes the attacks generated in upper layers like Medium Access Control (MAC), producing the same effect on the communication channel. In order to study the accuracy of the proposed epidemic model to estimate the propagation of jamming attacks, this paper uses the results of public simulations and experiments. It is of special interest the data obtained from experiments based on protocols such as Multi-Parent Hierarchical Protocol (MPH), Ad-hoc On-demand Distance Vector (AODV), and Dynamic Source Routing (DSR), working over the IEEE 802.15.4 standard. Then, using the formulation of the deterministic epidemiological model Susceptible–Infected–Recovered (SIR), together the abovementioned simulation, it has been seen that the proposed epidemic model could be used to estimate in that kind of IoT networks, the impact of the jamming attack in terms of attack severity and attack persistence.

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Crossref · 10.1016/j.comnet.2019.106945

Cites

29 of 29 references verified0 citation errors · 0 not checkable

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Citations

Cites

Research works printed but not linked: 2

  1. Mishra, 2014, A quarantine model on the spreading behavior of worms in wireless sensor network, Trans. IoT Cloud Comput., 2, 1
  2. Zhu, 2015, “Dynamical analysis and optimal control for a malware propagation model in an information network, Neurocomputing, 149, 1370, 10.1016/j.neucom.2014.08.060 · DOI

Cited by

See all 61

Metadata

Authors
López, M., Peinado, A., & Ortiz, A.
Affiliations
Universidad de Málaga (ES)
ORCID
Peinado, Alberto
Venue
Computer Networks
Publisher
Elsevier BV
Published
2019-12
Volume
165
Pages
106945
ISSN
1389-1286 (ISSN-L)
DOI
10.1016/j.comnet.2019.106945
OpenAlex
W2981181590
Cited by
61
References
29
Citations by year
2026: 10 · 2025: 7 · 2024: 9 · 2023: 9 · 2022: 12 · 2021: 10 · 2020: 4
FWCI
3.56
Type
journal article
Language
en
Open access
Licence
  • cc-by-nc-nd
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (tdm)
Locations
  • Computer Networks (publishedVersion)
  • Repositorio Institucional de la Universidad de Málaga (University of Málaga) (submittedVersion, cc-by-nc-nd)
Funders
  • European Regional Development Fund
  • University of the East (PGC2018-098813-B-C32, TIN2017-84844-C2-1-R)
  • Ministerio de Economía y Competitividad
  • Ministerio de Economía, Industria y Competitividad, Gobierno de España
  • Agencia Estatal de Investigación
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Security in Wireless Sensor Networks
  • Physical Sciences › Computer Science › Computer Networks and Communications › Mobile Ad Hoc Networks
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
Keywords
jamming attacks, Internet of Things, SIR model, attack propagation, wireless sensor networks, IEEE 802.15.4, AODV, dynamic source routing, attack severity, network security

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.