Detection of Insider Selective Forwarding Attack Based on Monitor Node and Trust Mechanism in WSN

Hu, Yu; Wu, Yonghong; Wang, Hongshuai

doi:10.4236/wsn.2014.611023

Cited by 32 publications

(20 citation statements)

References 11 publications

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“…More nodes lay near the SN (at the center) because they have to forward more data packets from nodes far from the SN in the multi-hop WSNs. Figure 15 is the simulation result with the energy consumption module [44] in wireless communication. DCA-SF offers the WSN a much longer lifetime than the IN monitoring scheme.…”

Section: Detection Results Compared With Other Schemesmentioning

confidence: 99%

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A Data Clustering Algorithm for Detecting Selective Forwarding Attack in Cluster-Based Wireless Sensor Networks

Liu

Dong

et al. 2019

Sensors

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In cluster-based wireless sensor networks, cluster heads (CHs) gather and fuse data packets from sensor nodes; then, they forward fused packets to the sink node (SN). This helps wireless sensor networks balance energy effectively and efficiently to prolong their lifetime. However, cluster-based WSNs are vulnerable to selective forwarding attacks. Compromised CHs would become malicious and launch selective forwarding attacks in which they drop part of or all the packets from other nodes. In this paper, a data clustering algorithm (DCA) for detecting a selective forwarding attack (DCA-SF) is proposed. It can capture and isolate malicious CHs that have launched selective forwarding attacks by clustering their cumulative forwarding rates (CFRs). The DCA-SF algorithm has been strengthened by changing the DCA parameters (Eps, Minpts) adaptively. The simulation results show that the DCA-SF has a low missed detection rate of 1.04% and a false detection rate of 0.42% respectively with low energy consumption.Keywords: data clustering algorithm; selective forwarding attack; cumulative forwarding rate; cluster-based WSN IntroductionA wireless sensor network (WSN) is a self-organizing network formed by a mass of small and cheap sensor nodes, which have low energy, poor computing ability, and small storage. The cluster-based WSN has been widely applied in large-scale data gathering WSNs [1,2]. In the dense cluster-based WSN, as shown in Figure 1, member nodes (MNs) send data packets to their cluster heads (CHs). Then, CHs forward these packets to the next-hop CHs until they reach the sink node (SN). In this way, each CH does not have to exchange data with the SN directly. On the one hand, the direct communication between the CH and SN may fail due to the long-distance or poor channels. On the other hand, in some conditions, the energy cost of multi-hop communication is less than that of direct long-distance communication. All the nodes in the network take turns to act as CHs, so the energy consumption can get balanced. The network lifetime also becomes longer.

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Section: Detection Results Compared With Other Schemesmentioning

confidence: 99%

“…We plan to employ DPC and SNN-DPC to cluster the data set in SN for testing their effects. Figure 15 is the simulation result with the energy consumption module [44] in wireless communication.…”

Section: Discussionmentioning

confidence: 99%

A Data Clustering Algorithm for Detecting Selective Forwarding Attack in Cluster-Based Wireless Sensor Networks

Liu

Dong

et al. 2019

Sensors

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“…Insider attack have received attention from researchers such as in [83] the authors proposed a mechanism that manages the network using a node, which monitor the network constantly. The proposed algorithm works by maintaining a dynamic threshold.…”

Section: Existing Solution and Discussionmentioning

confidence: 99%

Understanding Security Requirements and Challenges in Internet of Things (IoT): A Review

Hameed

Khan

Hameed

2019

Journal of Computer Networks and Communications

170

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Internet of Things (IoT) is realized by the idea of free flow of information amongst various low power embedded devices that use Internet to communicate with one another. It is predicted that the IoT will be widely deployed and it will find applicability in various domains of life. Demands of IoT have lately attracted huge attention and organizations are excited about the business value of the data that will be generated by the IoT paradigm. On the other hand, IoT have various security and privacy concerns for the end users that limit its proliferation. In this paper we have identified, categorized and discussed various security challenges and state of the art efforts to resolve these challenges. Keywords: Security in IoT, Privacy in IoT, DoS in IoT, Secure routing in IoTIn [17] the authors identified and discussed three representative sensing applications. They are personal sensing, designated sensing and community sensing each requiring heterogeneous security and privacy

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“…Therefore, the RSSI ranging described in section 'Shortening the network lifespan' is also use to detect suspicious nodes when Sybil attackers launch selective forwarding. The adaptive threshold model [9] is introduced to determine suspicious nodes as Sybil nodes if the trust value T of a suspicious node is low than the trust threshold. The trust value T [10] of a node is shown in equation 1 and the threshold of trust value is defined as equation 2.…”

Section: Selective Forwardingmentioning

confidence: 99%

Sybil Attack Detection Scheme Based on Data Flow Monitoring and RSSI ranging in WSN

Xu¹,

Wu²,

Duan³

2017

2017 2nd International Conference on Mechatronics and Information Technology (ICMIT 2017)

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Sybil attack is a destructive attack in wireless sensor networks, where a node illegitimately takes on multiple identities. Some scholars focus on how to defense against Sybil attack if the attackers launch selective forwarding attack. However, threat of network lifespan caused by Sybil attack is ignored. In this paper, we demonstrate that network lifespan is possible to be shortened by Sybil attackers and then we put forward a countermeasure against Sybil attack based on RSSI ranging and data flows monitoring. First, we detect suspicious nodes (we call them as node M and node N) if all of their common neighbors find node M and N have the same distance to them. Second, a suspicious node will be marked as Sybil node if its data flow is abnormal according to criterion. Simulation results show that proposed secure mechanism can effectively detect Sybil nodes with a misdetection rate of 6.7% and a false alarm rate of 3.3%. Additionally, proposed security scheme extends the network lifespan by 26.3%.

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Detection of Insider Selective Forwarding Attack Based on Monitor Node and Trust Mechanism in WSN

Cited by 32 publications

References 11 publications

A Data Clustering Algorithm for Detecting Selective Forwarding Attack in Cluster-Based Wireless Sensor Networks

A Data Clustering Algorithm for Detecting Selective Forwarding Attack in Cluster-Based Wireless Sensor Networks

Understanding Security Requirements and Challenges in Internet of Things (IoT): A Review

Sybil Attack Detection Scheme Based on Data Flow Monitoring and RSSI ranging in WSN

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