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DWT和AKD自动编码器的DDoS攻击检测方法研究
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TP393

基金项目:

国家自然科学基金 (62062020);贵州省教育厅自然科学研究项目(黔教科(2007)015号)


DDoS attack detection via DWT and AKD auto-encoder
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    摘要:

    针对DDoS网络流量攻击检测效率低及误报率高的问题,本文提出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)自动编码器神经网络的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:

    To address the low efficiency and high false alarm rate in detection of DDoS (Distributed Denial of Service) flood attacks,this paper proposes a DWT (Discrete Wavelet Transform) and AKD (Adaptive Knowledge Distillation) self-encoder neural network based approach to detect DDoS attacks.The approach uses the DWT to extract frequency features,the auto-encoder neural network to encode and classify the features,and the AKD to compress the model in order to achieve efficient detection of DDoS attacks.The results show that the approach has high detection efficiency for proxy server attacks,database vulnerabilities & TCP flood attacks,and UDP flood attacks,with low false alarm rate.

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王博,万良,刘明盛,孙菡迪. DWT和AKD自动编码器的DDoS攻击检测方法研究[J].南京信息工程大学学报(自然科学版),2023,15(4):419-428
WANG Bo, WAN Liang, LIU Mingsheng, SUN Handi. DDoS attack detection via DWT and AKD auto-encoder[J]. Journal of Nanjing University of Information Science & Technology, 2023,15(4):419-428

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  • 收稿日期:2022-09-19
  • 在线发布日期: 2023-07-06

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