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DAFL: Deep Adaptive Feature Learning for Network Anomaly Detection

  • Shujian Ji
  • Tongzheng Sun
  • Kejiang YeEmail author
  • Wenbo Wang
  • Cheng-Zhong Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11783)

Abstract

With the rapid development of the Internet and the growing complexity of the network topology, network anomaly has become more diverse. In this paper, we propose an algorithm named Deep Adaptive Feature Learning (DAFL) for traffic anomaly detection based on deep learning model. By setting proper feature parameters \(\theta \) on the neural network structure, DAFL can effectively generate low-dimensional new abstract features. Experimental results show the DAFL algorithm has good adaptability and robustness, which can effectively improve the detection accuracy and significantly reduce the detection time.

Keywords

Network anomaly detection Deep learning Feature learning 

Notes

Acknowledgment

This work is supported by the National Key R&D Program of China (No. 2018YFB1004804), National Natural Science Foundation of China (No. 61702492), Shenzhen Discipline Construction Project for Urban Computing and Data Intelligence, and Shenzhen Basic Research Program (No. JCYJ20170818153016513).

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Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Shujian Ji
    • 1
    • 2
  • Tongzheng Sun
    • 1
  • Kejiang Ye
    • 1
    Email author
  • Wenbo Wang
    • 3
  • Cheng-Zhong Xu
    • 4
  1. 1.Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Khoury College of Computer SciencesNortheastern UniversitySeattleUSA
  4. 4.Faculty of Science and TechnologyUniversity of MacauMacauChina

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