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Distributed Anomaly Detection Method in Wireless Sensor Networks Based on Temporal-Spatial QSSVM

  • Zhili Chen
  • Huarui WuEmail author
  • Huaji Zhu
  • Yisheng Miao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)

Abstract

In Wireless Sensor Networks (WSNs), abnormal sensing data is easily generated due to factors such as the harsh working environment, sensor faults and external events. In order to enhance the detection rate of abnormal data and reduce the false positive rate, we propose a distributed anomaly detection method using one-class quarter-sphere support vector machine (QSSVM) based on temporal-spatial fusion in WSNs. Firstly, according to the synthetic data, the temporal-spatial QSSVM model is trained to determine the relevant parameters. Secondly, the trained QSSVM model is used to classify the streaming data in WSNs, and the abnormal data types are classified into noise, faults and events. Finally, the method decides whether to update the classification model based on whether the new sample has an effect on the boundary of the hypersphere. The experimental results illustrate that the proposed method has a detection rate of 96% compared with other three methods, and the false positive rate is only 14%.

Keywords

Anomaly detection QSSVM Temporal-spatial Wireless sensor networks 

Notes

Acknowledgement

This work was supported by Natural Science Foundation of China (61471067, 61571051) and Beijing Natural Science Foundation (4172024, 4172026).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zhili Chen
    • 1
    • 2
  • Huarui Wu
    • 3
    Email author
  • Huaji Zhu
    • 3
  • Yisheng Miao
    • 3
  1. 1.National Engineering Research Center for Information Technology in AgricultureBeijingChina
  2. 2.College of Information and Electrical EngineeringChina Agricultural UniversityBeijingChina
  3. 3.Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry SciencesBeijingChina

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