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<p>For the purpose of identifying attacks on the network systems, a monitoring method is essential. Identification of DDOS attacks is one of the most important concerns now a days in wireless networks. This paper proposed a security mechanism to identify DDoS attacks using enhanced AutoEncoder and Deep Neural Network (AE &amp; DNN). In traditional methods, it is very difficult to classify the normal traffic in the network from the malicious one. Therefore some attacks will exist for a long time. So that the network will get affected by various attacks. To overcome these problems AutoEncoder is combined with Deep Neural Network, where AutoEncoder is mainly utilized for feature extraction and Deep Neural Network is used for classification. For validation, experiments have been done on the WSN-DS, CICIDS2017, and NSL-KDD standard datasets. From the experiments made, the proposed method outperformed than the CICIDS2017, and NSL-KDD datasets.</p>

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Enhanced autoencoder and deep neural network (AE&DNN) method for ddos attack recognition

  • 期刊论文

  • 发表日期：

    2025年3月15日

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  • P. J. Beslin Pajila,
  • Y. Harold Robinson,
  • Udayakumar Allimuthu,
  • E. Golden Julie,
  • Lijetha. C. Jaffrin,
  • B. Gracelin Sheena

摘要

For the purpose of identifying attacks on the network systems, a monitoring method is essential. Identification of DDOS attacks is one of the most important concerns now a days in wireless networks. This paper proposed a security mechanism to identify DDoS attacks using enhanced AutoEncoder and Deep Neural Network (AE & DNN). In traditional methods, it is very difficult to classify the normal traffic in the network from the malicious one. Therefore some attacks will exist for a long time. So that the network will get affected by various attacks. To overcome these problems AutoEncoder is combined with Deep Neural Network, where AutoEncoder is mainly utilized for feature extraction and Deep Neural Network is used for classification. For validation, experiments have been done on the WSN-DS, CICIDS2017, and NSL-KDD standard datasets. From the experiments made, the proposed method outperformed than the CICIDS2017, and NSL-KDD datasets.