Data Collection for Security Measurement in Wireless Sensor Networks: A Survey

Tutkimustuotos: Lehtiartikkeli

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Data Collection for Security Measurement in Wireless Sensor Networks : A Survey. / Xie, Haomeng; Yan, Zheng; Yao, Zhen; Atiquzzaman, Mohammed.

julkaisussa: IEEE Internet of Things Journal, Vuosikerta 6, Nro 2, 04.2019, s. 2205-2224.

Tutkimustuotos: Lehtiartikkeli

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Xie, Haomeng ; Yan, Zheng ; Yao, Zhen ; Atiquzzaman, Mohammed. / Data Collection for Security Measurement in Wireless Sensor Networks : A Survey. Julkaisussa: IEEE Internet of Things Journal. 2019 ; Vuosikerta 6, Nro 2. Sivut 2205-2224.

Bibtex - Lataa

@article{5aa321c4639546d6b8456455ed1db2ec,
title = "Data Collection for Security Measurement in Wireless Sensor Networks: A Survey",
abstract = "Wireless Sensor Network (WSN) is an indispensible part of IoT that has been applied in many fields to monitor environments and collect data from surroundings. However, WSNs are highly susceptible to attacks due to its unique characteristics: large-scale, self-organization, dynamic topology and constrained resources. A number of systems have been proposed to effectively detect varieties of attacks in WSNs. However, most previous surveys on WSN attacks focus on detection methods for only one or two types of attacks and lack detailed performance analysis. Additionally, the literature lacks comprehensive studies on security-related data (in short security data) collection in WSNs. In this paper, we first provide an overview of WSNs and classify the attacks in WSNs based on protocol stack layers. For the purpose of WSN security measurement, we then research attack detection methods of eleven mainstream attacks. We extract security data that play an important role for detecting security anomaly towards security measurement. We further elaborate the advantages and disadvantages of the existing detection methods based on a number of evaluation criteria. Finally, we highlight a number of open problems in this research field based on our thorough survey and conclude this paper with possible future research directions.",
keywords = "attack detection, Data collection, Internet of Things, Internet of Things (IoT), Jamming, Physical layer, Random access memory, Security, security data collection, security measurement, Wireless Sensor Network (WSN)., Wireless sensor networks",
author = "Haomeng Xie and Zheng Yan and Zhen Yao and Mohammed Atiquzzaman",
year = "2019",
month = "4",
doi = "10.1109/JIOT.2018.2883403",
language = "English",
volume = "6",
pages = "2205--2224",
journal = "IEEE Internet of Things Journal",
issn = "2327-4662",
publisher = "Institute of Electrical and Electronics Engineers",
number = "2",

}

RIS - Lataa

TY - JOUR

T1 - Data Collection for Security Measurement in Wireless Sensor Networks

T2 - A Survey

AU - Xie, Haomeng

AU - Yan, Zheng

AU - Yao, Zhen

AU - Atiquzzaman, Mohammed

PY - 2019/4

Y1 - 2019/4

N2 - Wireless Sensor Network (WSN) is an indispensible part of IoT that has been applied in many fields to monitor environments and collect data from surroundings. However, WSNs are highly susceptible to attacks due to its unique characteristics: large-scale, self-organization, dynamic topology and constrained resources. A number of systems have been proposed to effectively detect varieties of attacks in WSNs. However, most previous surveys on WSN attacks focus on detection methods for only one or two types of attacks and lack detailed performance analysis. Additionally, the literature lacks comprehensive studies on security-related data (in short security data) collection in WSNs. In this paper, we first provide an overview of WSNs and classify the attacks in WSNs based on protocol stack layers. For the purpose of WSN security measurement, we then research attack detection methods of eleven mainstream attacks. We extract security data that play an important role for detecting security anomaly towards security measurement. We further elaborate the advantages and disadvantages of the existing detection methods based on a number of evaluation criteria. Finally, we highlight a number of open problems in this research field based on our thorough survey and conclude this paper with possible future research directions.

AB - Wireless Sensor Network (WSN) is an indispensible part of IoT that has been applied in many fields to monitor environments and collect data from surroundings. However, WSNs are highly susceptible to attacks due to its unique characteristics: large-scale, self-organization, dynamic topology and constrained resources. A number of systems have been proposed to effectively detect varieties of attacks in WSNs. However, most previous surveys on WSN attacks focus on detection methods for only one or two types of attacks and lack detailed performance analysis. Additionally, the literature lacks comprehensive studies on security-related data (in short security data) collection in WSNs. In this paper, we first provide an overview of WSNs and classify the attacks in WSNs based on protocol stack layers. For the purpose of WSN security measurement, we then research attack detection methods of eleven mainstream attacks. We extract security data that play an important role for detecting security anomaly towards security measurement. We further elaborate the advantages and disadvantages of the existing detection methods based on a number of evaluation criteria. Finally, we highlight a number of open problems in this research field based on our thorough survey and conclude this paper with possible future research directions.

KW - attack detection

KW - Data collection

KW - Internet of Things

KW - Internet of Things (IoT)

KW - Jamming

KW - Physical layer

KW - Random access memory

KW - Security

KW - security data collection

KW - security measurement

KW - Wireless Sensor Network (WSN).

KW - Wireless sensor networks

UR - http://www.scopus.com/inward/record.url?scp=85057370380&partnerID=8YFLogxK

U2 - 10.1109/JIOT.2018.2883403

DO - 10.1109/JIOT.2018.2883403

M3 - Review Article

VL - 6

SP - 2205

EP - 2224

JO - IEEE Internet of Things Journal

JF - IEEE Internet of Things Journal

SN - 2327-4662

IS - 2

ER -

ID: 30362469