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计算机工程 ›› 2020, Vol. 46 ›› Issue (4): 213-219. doi: 10.19678/j.issn.1000-3428.0054066

• 移动互联与通信技术 • 上一篇    下一篇

一种基于分层聚合的分布式异常数据检测方案

许春杰a, 吴蒙b, 杨立君c   

  1. 南京邮电大学 a. 计算机学院;b. 通信与信息工程学院;c. 物联网学院, 南京 210023
  • 收稿日期:2019-03-04 修回日期:2019-04-20 出版日期:2020-04-15 发布日期:2019-05-29
  • 作者简介:许春杰(1995-),男,硕士研究生,主研方向为异常检测、渗透测试、深度学习;吴蒙,教授;杨立君,讲师。
  • 基金资助:
    国家自然科学基金青年基金(61602263);江苏省基础研究计划(自然科学基金)青年基金(BK20160916);中国博士后基金(2017M621798);南京邮电大学引进人才科研启动基金(NY216020)。

A Distributed Abnormal Data Detection Scheme Based on Hierarchical Aggregation

XU Chunjiea, WU Mengb, YANG Lijunc   

  1. a. School of Computer Science;b. School of Telecommunications and Information Engineering;c. School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
  • Received:2019-03-04 Revised:2019-04-20 Online:2020-04-15 Published:2019-05-29

摘要: 在无线传感器网络中,由于传感器节点的带宽、功率、计算能力有限,传统的集中式方案难以区分海量数据中的异常数据。为解决此问题,提出一种基于多层分布式无线传感器网络的异常数据检测方案。在节点层级采用K-Means++算法对数据进行聚类,执行簇合并算法以减少数据传输量,在网关节点执行基于KNN的异常簇检测算法,将正常簇信息返回至底层节点进行局部检测,从而区分异常数据。在高斯数据集与IBRL数据集上的实验结果表明,该方案检测率高于98%,且能显著降低通信消耗。

关键词: 异常检测, 无监督学习, 无线传感器网络, 分布式处理, 聚类

Abstract: Due to the limited bandwidth,power and computational capability of sensor nodes in the Wireless Sensor Network(WSN),the traditional scheme hardly distinguish the abnormal data accurately when faced with massive data.To address this problem,this paper proposes an abnormal data detection scheme based on hierarchical distributed WSN.The scheme clusters the data at the node level by K-Means++ algorithm.Then cluster merging algorithm is used to reduce the amount of data transmission.The KNN-based abnormal cluster detection algorithm is performed on the gateway node,so as to return the normal cluster information to the underlying node for local detection,thus identifying the abnormal data.Experimental results on the Gaussian and IBRL datasets show that the detection rate of the proposed scheme is higher than 98%,and its communication consumption can be significantly reduced.

Key words: abnormal detection, unsupervised learning, Wireless Sensor Network(WSN), distributed processing, clustering

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