CHEMAS: Identify suspect nodes in selective forwarding attacks

Xiao, Bin; Yu, Bo; Gao, Chuanshan

doi:10.1016/j.jpdc.2007.04.014

Cited by 129 publications

(103 citation statements)

References 14 publications

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“…Figure 7 provides an example of a node that is suspect and detected by an intermediate node. First, Xiao, Yu, and Gao [48] proposed a checkpoint-based method.…”

Section: Fig 7: Suspect Nodes Identification-redrawn [48]mentioning

confidence: 99%

Comparative Analysis of Selective Forwarding Attacks over Wireless Sensor Networks

Alajmi¹,

Elleithy²

2015

IJCA

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The potential use of wireless sensor networks (WSNs) in many technological situations is extensive. WSNs are employed in numerous areas, such as battlefields, traffic surveillance, healthcare, and environmental monitoring. Many studies have been conducted to improve various aspects of the performance of WSNs, such as energy efficiency, quality of service (QoS), reliability, mobility, scalability, and extended network lifetime. However, few studies have sought to advance the security of WSNs by focusing on medium access control (MAC), physical, and transport stacks. The network layer is the middle layer that coordinates the lower layers and the upper layers. Thus, the network layer is of paramount significance to the security of WSNs to prevent exploitation of their confidentiality, privacy, availability, integrity, and authenticity. The objective of this study is to address the security laps of WSNs on the network layer, particularly selective forwarding attacks. This paper includes a survey performed from 2003 to 2014. Significant constraints and hazards of physical attacks at the network layer are extensively addressed. The survey includes a benchmark for the comparison of existing approaches for handling the security of WSNs on the network layer and their challenges. Future directions are also comprehensively highlighted in the document. This study will help researchers to understand attacks on the network layer. KeywordsWireless sensor network, selective forwarding, and denial of services attacks.

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“…Figure 7 provides an example of a node that is suspect and detected by an intermediate node. First, Xiao, Yu, and Gao [48] proposed a checkpoint-based method.…”

Section: Fig 7: Suspect Nodes Identification-redrawn [48]mentioning

confidence: 99%

Comparative Analysis of Selective Forwarding Attacks over Wireless Sensor Networks

Alajmi¹,

Elleithy²

2015

IJCA

View full text Add to dashboard Cite

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“…However, preservation of information and sensitive data is one of the biggest challenges in sensor networks [17][18][19]. Due to the high costs, the hardware of sensor nodes is relatively simple and is often unattended in hostile environment, hence prone to different kinds of attacks [20][21][22]. According to the statistics, there are over 30 kinds of attacks against the sensor network [21,23], including black attack, clone attack, selective forwarding attack, and false data injection attack [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the high costs, the hardware of sensor nodes is relatively simple and is often unattended in hostile environment, hence prone to different kinds of attacks [20][21][22]. According to the statistics, there are over 30 kinds of attacks against the sensor network [21,23], including black attack, clone attack, selective forwarding attack, and false data injection attack [20][21][22][23]. It is necessary to provide solid as well as evident solutions as countermeasures against threats in sensor networks; otherwise the application of sensor networks will be hindered [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…According to the statistics, there are over 30 kinds of attacks against the sensor network [21,23], including black attack, clone attack, selective forwarding attack, and false data injection attack [20][21][22][23]. It is necessary to provide solid as well as evident solutions as countermeasures against threats in sensor networks; otherwise the application of sensor networks will be hindered [20][21][22][23]. For example, black hole attack [23] is such an attack behavior which can drop all the data transferred through the black holes, and the selective forwarding attack (SFA) [20,24] can intelligently drop some packets to damage network which is difficult to detect and defense such attack [23].…”

Section: Introductionmentioning

confidence: 99%

“…It is necessary to provide solid as well as evident solutions as countermeasures against threats in sensor networks; otherwise the application of sensor networks will be hindered [20][21][22][23]. For example, black hole attack [23] is such an attack behavior which can drop all the data transferred through the black holes, and the selective forwarding attack (SFA) [20,24] can intelligently drop some packets to damage network which is difficult to detect and defense such attack [23]. The false data injection attack produces much false data to consume the precious energy of the sensor network, which will cause the advanced death of network exception [21,23].…”

Section: Introductionmentioning

confidence: 99%

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An Aggregate Signature Based Trust Routing for Data Gathering in Sensor Networks

Tang

Liu

Zhao

et al. 2018

Security and Communication Networks

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An Aggregate Signature based Trust Routing (ASTR) scheme is proposed to guarantee safe data collection in WSNs. In ASTR scheme, firstly, the aggregate signature approach is used to aggregate data and keep data integrity. What is more important, a light aggregate signature based detour routing scheme is proposed to send abstract information which includes the data sending time and ID of data, nodes' ID to the sink over different paths which can verify whether the data reaches the sink safely. In addition, the trust of a path is evaluated according to the success rate of the path. The trust of paths susceptible to frequent attack will be lowered and the path with high trust will be selected for data routing to avoid data gathering through low trust path and thereby increase the success rate of data gathering. Our comprehensive performance analysis has shown that, the ASTR scheme is able to effectively ensure an increase in success rate of data transmission by 23.23%, reduce the data amount loaded by the node by 53.59%, reduce the redundant data by 41.70%.

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Internet of Things Security and Privacy

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2022

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CHEMAS: Identify suspect nodes in selective forwarding attacks

Cited by 129 publications

References 14 publications

Comparative Analysis of Selective Forwarding Attacks over Wireless Sensor Networks

Comparative Analysis of Selective Forwarding Attacks over Wireless Sensor Networks

An Aggregate Signature Based Trust Routing for Data Gathering in Sensor Networks

Internet of Things Security and Privacy

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