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DWT和AKD的DDoS攻击检测方法研究
作者:
作者单位:

贵州大学计算机科学与技术学院

中图分类号:

TP393

基金项目:

国家自然科学基金项目（面上项目，重点项目，重大项目）


Research on DDoS attack detection method with DWT and AKD Auto-encoder
Author:
Affiliation:

College of Computer Science and Technology, Guizhou University

Fund Project:

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

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    摘要:

    针对DDoS(Distributed denial of service)网络流量攻击检测效率低及误报率高的问题，本文提出了一种基于离散小波变换(Discrete Wavelet Transform DWT)和自适应知识蒸馏(Adaptive Knowledge Distillation AKD)自编码器神经网络的DDoS攻击检测方法。该方法利用离散小波变换提取频率特征，由自动编码器神经网络进行特征编码并实现分类，通过自适应知识蒸馏压缩模型，以实现高效检测DDoS攻击流量。研究结果表明，该方法对代理服务器攻击、数据库漏洞和TCP洪水攻击、UDP洪水攻击具有较高的检测效率，并且具有较低的误报率。

    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.

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王博,万良,刘明盛,孙菡迪. DWT和AKD的DDoS攻击检测方法研究[J].南京信息工程大学学报,,():

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  • 收稿日期:2022-09-19
  • 最后修改日期:2022-10-13
  • 录用日期:2022-11-09

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