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Anomaly Detection Based on a Multi-class CUSUM Algorithm for WSN

机译:基于多级CUSUM算法的异常检测WSN

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—Security is one of the most important research issues in wireless sensor networks (WSN) applications. Given that the single detection threshold of the cumulative sum (CUSUM) algorithm causes longer detection delays and a lower detection rate, a multi-class CUSUM algorithm is hereby proposed. Firstly a maximum and minimum thresholds, which sensor nodes are able to reach during sending packet, are set to eliminate abnormal flow to enhance the detection efficiency. Secondly, CUSUM algorithms of different thresholds, all of which are selected according to the mean of traffic sequences, are applied to detect anomalous nodes. This study aims to optimize threshold parameters, the size of which increases with the number of traffic sequence. Using the NS2 tool, the different values of network traffic sequence are generated and simulated. Based on these values, the detection rates of the CUSUM algorithm and multi-class CUSUM algorithms, as well as their false positive rates, are then evaluated. Results show that the proposed algorithm achieves a higher and more accurate rate of detection and lower false positive rates than do the current important intrusion detection schemes of WSN.
机译:-Security是无线传感器网络(WSN)应用中最重要的研究问题之一。鉴于累积和累积量(CUSUM)算法的单个检测阈值导致较长的检测延迟和较低的检测率,特此提出了多级CUSUM算法。首先是最大和最小阈值,传感器节点能够在发送数据包期间达到,被设置为消除异常流以增强检测效率。其次,应用了根据交通序列的平均值选择的不同阈值的CuSum算法,用于检测异常节点。本研究旨在优化阈值参数,其大小随着流量序列的数量而增加。使用NS2工具,生成和模拟网络流量序列的不同值。基于这些值,然后评估CUSUM算法和多级CUSUM算法的检测速率以及它们的假阳性率。结果表明,该算法达到了更高且更准确的检测率和比WSN的当前重要的入侵检测方案更高的误差率。

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