Abstract

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.

Highlights

  • A 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 data clustering algorithm (DCA)-SF based on the DP-DBSCAN utilizes the clustering points of cumulative forwarding rates (CFRs) to detect attacks, which is different from E-DBSCAN

  • The total forwarding rates of these three malicious nodes are 0.6299, 0.1468, and 0.6051 respectively, and assume that k described in Section 3.4.2 is set to five in this simulation

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Summary

Introduction

A 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. If a for malicious node cannot detected from that the caused by poor channel selective quality [8] This makes it easy malicious nodes to be hide their and isolated it would continue data packets. Someis not included in thetake dataadvantage set collection process This causes a normal node a lower cumulative detection schemes of the clustering algorithm [14], but thewith channel quality is not forwarding to be misjudged as a malicious node to a node poor channel. Is where to find aout anomalous performance, but in practice, theythe result a low correct detection rate poor channel CFRs exists.of malicious nodes under same the channel normal nodes.

Related Work
Frame of Detection Mechanism
Introduction of DBSCAN
Complexity Analysis of DP-DBSCAN
Two Key Notes
Process of DCA-SF
Simulation Parameters
Results and Analysis
Relationship between average stable rounds whichthe the detection results
10. Average
11. Average
Results
Detection Results Compared with Other DCAs
13. Comparison
Detection Results Compared with Other Schemes
Energy with Other
15. Comparison of network
Conclusion and Future
Full Text
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