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Behavior Similarity Awared Abnormal Service Identification Mechanism

  • Yingyan Jiang
  • Yezhao Chen
  • Feifei Hu
  • Guoyi Zhang
  • Peng LinEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)

Abstract

In order to maintain network security, it is very important to identify services with abnormal behavior and take targeted measures to prevent abnormal behaviors. We propose abnormal service identification mechanism based on behavior similarity. This method proposes a formula for service behavior similarity calculation of flow ports for services with correlation. And then k-similarity clustering algorithm is proposed to find abnormal service behaviors. Meanwhile, we analyse outliers to improve the accuracy of clustering results. At last, the experimental results show that k-similarity clustering algorithm can differentiate abnormal services accurately.

Keywords

Behavior similarity Abnormal service K-similarity clustering algorithm 

Notes

Acknowledgment

This work was financially supported by Research and Application on Intelligent Operation Management Technology in Voice Exchange Network (036000KK52160009) hosted by Power Grid Dispatching Control Center of Guangdong Power Grid Co., Ltd., China Southern Power Grid.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Yingyan Jiang
    • 1
  • Yezhao Chen
    • 1
  • Feifei Hu
    • 2
  • Guoyi Zhang
    • 2
  • Peng Lin
    • 3
    Email author
  1. 1.Power Grid Dispatching Control Center of Guangdong Power Grid Co., Ltd.GuangzhouChina
  2. 2.Power Grid Dispatching Control Center of China Southern Power GridGuangzhouChina
  3. 3.Beijing Vectinfo Technologies Co., Ltd.BeijingChina

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