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DDoS Detection Method Based on Improved Generalized Entropy

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Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 153))

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Abstract

With the rapid development of network technology, network security is facing serious problems. Distributed Denial of Service (DDoS) attack is one of the most difficult security threats to guard against. In this paper, we propose a DDoS detection method based on improved generalized entropy. The model includes a preliminary detection module based on improved generalized entropy and a DDoS detector based on deep neural networks (DNN). The preliminary detection module filters as much normal traffic as possible while ensuring the accuracy of the model by calculating the generalized entropy threshold of the traffic. The DNN-based DDoS detector takes the filtered data as input and detects DDoS attacks more accurately. The experimental results show that the method achieves more than 99% accuracy, precision, and recall on the dataset of this paper.

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Acknowledgement

This work is supported by the National Natural Science Foundation of China (Grant No.62072109, and No.U1804263), the Natural Science Foundation of Fujian Province(Grant No.2021J01625, No.2021J01616), Major Science and Technology project of Fujian Province(Grant No.2021HZ0115).

Author information

Authors and Affiliations

  1. College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China

    Jiaqi Li, Hui Chen & Yanhua Liu

  2. State Grid Info-Telecom Great Power Science and Technology CO., LTD., Fuzhou, 350000, China

    Xu Yang, Haoqiang Lin & Xinqing Chen

Authors
  1. Jiaqi Li
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  2. Xu Yang
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  3. Hui Chen
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  4. Haoqiang Lin
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  5. Xinqing Chen
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  6. Yanhua Liu
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Corresponding authors

Correspondence to Jiaqi Li or Yanhua Liu .

Editor information

Editors and Affiliations

  1. Division of Intelligent Future Technologies, Mälardalen University, Västerås, Västmanlands Län, Sweden

    Ning Xiong

  2. Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, Middlesex, UK

    Maozhen Li

  3. School of Information Science and Technology, Hunan University, Changsha, Hunan, China

    Kenli Li

  4. School of Information Science and Technology, Hunan University, Changsha, Hunan, China

    Zheng Xiao

  5. College of Computer and Data Science, Fuzhou University, Fuzhou, Fujian, China

    Longlong Liao

  6. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Li, J., Yang, X., Chen, H., Lin, H., Chen, X., Liu, Y. (2023). DDoS Detection Method Based on Improved Generalized Entropy. In: Xiong, N., Li, M., Li, K., Xiao, Z., Liao, L., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 153. Springer, Cham. https://doi.org/10.1007/978-3-031-20738-9_59

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