Research on network intrusion detection methods is one of the important aspects in the field of cybersecurity. In this paper, we propose a network intrusion detection method based on stacked contractive autoencoder and gated convolution to address the challenge of handling high-dimensional network data in the field of intrusion detection. This method utilizes stacked contractive autoencoder to achieve data dimensionality reduction and employs gated convolution algorithm to establish a trainable dynamic feature selection mechanism, allowing for better representation of important features. Finally, the extracted features are fed into a classifier for malicious attack detection. The proposed method was trained and tested using two intrusion detection datasets, namely NSL-KDD and UNSW-NB15, achieving overall accuracies of 89.4% and 97.9% respectively.


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    Title :

    Network Intrusion Detection Method Based on Stacked Contractive Auto-Encoder and Gated Convolution


    Contributors:
    Li, Shuai (author) / Wang, Shang (author) / Dang, Fangfang (author) / Li, Dingding (author) / Wang, Ruimin (author)


    Publication date :

    2023-10-11


    Size :

    2788977 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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