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Classification approach to avoid link failures in wireless sensor networks in mobile virtual communities and teleworking

机译:分类方法,可避免在移动虚拟社区和远程办公的无线传感器网络中出现链路故障

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Security issues are the primary issues in wireless sensor networks (WSN) due to its large network coverage and number of nodes. The attackers in WSN attack the particular node which has a low energy level and converts this node into malicious node. The formation of malicious node is the primary reason for link failures between nodes. This paper proposes an efficient methodology to detect the malicious nodes in WSN using feed forward back propagation neural network classifier. This classifier differentiates the malicious node from trusty node based on the extracted features of the test node. The performance of the proposed malicious node detection system is analysed in terms of detection rate, packet delivery ratio (PDR) and latency. The experimental results are compared with state-of-art methods.
机译:由于无线传感器网络(WSN)的网络覆盖范围广且节点数量众多,因此安全问题是其主要问题。 WSN中的攻击者攻击能量水平较低的特定节点,并将其转换为恶意节点。恶意节点的形成是节点之间链接失败的主要原因。本文提出了一种使用前馈传播神经网络分类器检测WSN中恶意节点的有效方法。该分类器根据测试节点的提取特征将恶意节点与可信赖节点区分开。根据检测率,数据包传输率(PDR)和延迟来分析所提出的恶意节点检测系统的性能。将实验结果与最新方法进行了比较。

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