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A Generalized Threat Model for Visual Sensor Networks

Today, visual sensor networks (VSNs) are pervasively used in smart environments such as intelligent homes, industrial automation or surveillance. A major concern in the use of sensor networks in general is their reliability in the presence of security threats and cyberattacks. Compared to traditiona...

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Detalles Bibliográficos
Autores principales: Simonjan, Jennifer, Taurer, Sebastian, Dieber, Bernhard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7374518/
https://www.ncbi.nlm.nih.gov/pubmed/32605274
http://dx.doi.org/10.3390/s20133629
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author Simonjan, Jennifer
Taurer, Sebastian
Dieber, Bernhard
author_facet Simonjan, Jennifer
Taurer, Sebastian
Dieber, Bernhard
author_sort Simonjan, Jennifer
collection PubMed
description Today, visual sensor networks (VSNs) are pervasively used in smart environments such as intelligent homes, industrial automation or surveillance. A major concern in the use of sensor networks in general is their reliability in the presence of security threats and cyberattacks. Compared to traditional networks, sensor networks typically face numerous additional vulnerabilities due to the dynamic and distributed network topology, the resource constrained nodes, the potentially large network scale and the lack of global network knowledge. These vulnerabilities allow attackers to launch more severe and complicated attacks. Since the state-of-the-art is lacking studies on vulnerabilities in VSNs, a thorough investigation of attacks that can be launched against VSNs is required. This paper presents a general threat model for the attack surfaces of visual sensor network applications and their components. The outlined threats are classified by the STRIDE taxonomy and their weaknesses are classified using CWE, a common taxonomy for security weaknesses.
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spelling pubmed-73745182020-08-05 A Generalized Threat Model for Visual Sensor Networks Simonjan, Jennifer Taurer, Sebastian Dieber, Bernhard Sensors (Basel) Article Today, visual sensor networks (VSNs) are pervasively used in smart environments such as intelligent homes, industrial automation or surveillance. A major concern in the use of sensor networks in general is their reliability in the presence of security threats and cyberattacks. Compared to traditional networks, sensor networks typically face numerous additional vulnerabilities due to the dynamic and distributed network topology, the resource constrained nodes, the potentially large network scale and the lack of global network knowledge. These vulnerabilities allow attackers to launch more severe and complicated attacks. Since the state-of-the-art is lacking studies on vulnerabilities in VSNs, a thorough investigation of attacks that can be launched against VSNs is required. This paper presents a general threat model for the attack surfaces of visual sensor network applications and their components. The outlined threats are classified by the STRIDE taxonomy and their weaknesses are classified using CWE, a common taxonomy for security weaknesses. MDPI 2020-06-28 /pmc/articles/PMC7374518/ /pubmed/32605274 http://dx.doi.org/10.3390/s20133629 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Simonjan, Jennifer
Taurer, Sebastian
Dieber, Bernhard
A Generalized Threat Model for Visual Sensor Networks
title A Generalized Threat Model for Visual Sensor Networks
title_full A Generalized Threat Model for Visual Sensor Networks
title_fullStr A Generalized Threat Model for Visual Sensor Networks
title_full_unstemmed A Generalized Threat Model for Visual Sensor Networks
title_short A Generalized Threat Model for Visual Sensor Networks
title_sort generalized threat model for visual sensor networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7374518/
https://www.ncbi.nlm.nih.gov/pubmed/32605274
http://dx.doi.org/10.3390/s20133629
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