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Abnormal Traffic Detection of Industrial Edge Network Based on Deep Nature Learning

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12737))

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Abstract

In view of the network and application security risks in the field of industrial Internet edge computing, a method for classifying abnormal traffic of industrial edge network based on Convolution Neural Network (CNN) is presented, which is designed by using feature self-learning through analyzing the substantial flow content and protocol hierarchy characteristics of edge network packets. Authors present an abnormal traffic detection model for industrial edge network based on CNN, by using the preprocessed raw traffic data as sample data to directly learn features. The experimental results show that the average accuracy of the trained and optimized model is 98.76%, which can meet the practical application standard of the industrial edge network anomaly traffic detection task.

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Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Shenyang University of Technology, Shenyang, 110870, China

    Qi Liu & Xibo Wang

  2. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China

    Qi Liu, Bowen Zhang, Jianming Zhao & Chuanzhi Zang

  3. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China

    Bowen Zhang, Jianming Zhao & Chuanzhi Zang

  4. State Grid Liaoning Electric Power Research Institute, Shenyang, 110004, China

    Tong Li

Authors
  1. Qi Liu
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  2. Bowen Zhang
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  3. Jianming Zhao
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  4. Chuanzhi Zang
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  5. Xibo Wang
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  6. Tong Li
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Corresponding author

Correspondence to Chuanzhi Zang .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Liu, Q., Zhang, B., Zhao, J., Zang, C., Wang, X., Li, T. (2021). Abnormal Traffic Detection of Industrial Edge Network Based on Deep Nature Learning. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12737. Springer, Cham. https://doi.org/10.1007/978-3-030-78612-0_50

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  • DOI: https://doi.org/10.1007/978-3-030-78612-0_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78611-3

  • Online ISBN: 978-3-030-78612-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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