一种用于检测基于簇的无线传感器网络中选择性转发攻击的数据聚类算法。

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

出版信息

Sensors (Basel). 2019 Dec 19;20(1):23. doi: 10.3390/s20010023.

DOI:10.3390/s20010023

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6982835/

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.

摘要

在基于簇的无线传感器网络中，簇头（CH）从传感器节点收集和融合数据包；然后，它们将融合后的数据包转发到汇聚节点（SN）。这有助于无线传感器网络有效地平衡能量，延长其寿命。然而，基于簇的 WSN 容易受到选择性转发攻击。受损的 CH 可能会变得恶意，并发起选择性转发攻击，其中它们会丢弃来自其他节点的部分或全部数据包。在本文中，提出了一种用于检测选择性转发攻击的数据聚类算法（DCA）（DCA-SF）。它可以通过聚类它们的累积转发率（CFR）来捕获和隔离发起选择性转发攻击的恶意 CH。通过自适应地改变 DCA 参数（Eps、Minpts），增强了 DCA-SF 算法。仿真结果表明，DCA-SF 的漏报率为 1.04%，误报率为 0.42%，能量消耗低。