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Research on DDoS attack detection method with DWT and AKD Auto-encoder
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College of Computer Science and Technology, Guizhou University

Clc Number:

TP393

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    Abstract:

    For the problem of low efficiency and high false alarm rate of DDoS (Distributed denial of service) network traffic attack detection, this paper proposes a DDoS attack detection method based on Discrete Wavelet Transform DWT and Adaptive Knowledge Distillation AKD. Distillation AKD) self-encoder neural network based DDoS attack detection method. The method uses discrete wavelet transform to extract frequency features, the auto-encoder neural network to encode and classify the features, and the adaptive knowledge distillation to compress the model in order to achieve efficient detection of DDoS attack traffic. The research results show that the method has high detection efficiency for proxy server attacks, database vulnerabilities and TCP flood attacks, UDP flood attacks, and has a low false alarm rate.

    Reference
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History
  • Received:September 19,2022
  • Revised:October 13,2022
  • Adopted:November 09,2022
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