{"id":3831,"date":"2026-04-25T22:58:41","date_gmt":"2026-04-25T14:58:41","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=3831"},"modified":"2026-06-10T12:49:49","modified_gmt":"2026-06-10T04:49:49","slug":"jase-202609-32-013","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202609-32-013","title":{"rendered":"Deep Learning-Based Network Intrusion Detection and Prevention System"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 32<\/a><\/p>\n<\/div>\n\n\n\n

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Huang Nana<\/i><\/a><\/p>\n\n\n\n

School of Culture Communication, Henan Vocational Institute of Arts,Zheng Zhou Henan 451464 ,China<\/p>\n<\/div>\n\n\n\n

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Received: February 16, 2026
Accepted: March 27, 2026
Publication Date: April 25, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Detection performance of different models across five zero-day attack test rounds showing the stability of the proposed system<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:  BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202609_32.013<\/a>  <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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Current network intrusion detection systems struggle with feature representation, unknown attack detection, and coordinated response. This paper proposes an intelligent system that fuses NetFlow and payload features, employs a three-level detection engine (deep autoencoder, Transformer, GNN), and integrates with software defined networking for real-time mitigation and adaptive feedback-driven model improvement. Experiments on amixed dataset combining the CIC-IDS2018 and UNSW-NB15 show a detection rate of 98.7%, a false positive rate of 0.86%, and an average detection rate of 87.04% for unknown attacks, with real-time interception success reaching 99%.<\/p>\n\n\n\n

Keywords: Network Intrusion Detection and Prevention; Feature Fusion; Graph Neural Network; Software-Defined Network<\/em><\/p>\n\n\n\n

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