{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T07:47:28Z","timestamp":1763452048506,"version":"3.45.0"},"reference-count":30,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T00:00:00Z","timestamp":1762819200000},"content-version":"vor","delay-in-days":10,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Security and Privacy"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Network intrusion detection systems (NIDS), a crucial part of overall network security, are experiencing previously unheard\u2010of difficulties as a result of the quick advancement of technological developments. The complex consists and evolving threats are too much for conventional signature\u2010based and anomaly\u2010based intrusion detection techniques to handle. This paper suggests DeepNIDS, a deep learning\u2010based network intrusion detection system, to enhance NIDS performance. The method reduces the false alarm rate and enhances the ability to recognize unexpected assaults by combining algorithms such as generative adversarial networks (GANs), long short\u2010term memory networks (LSTMs), and convolutional neural networks (CNNs). Using public datasets including KDD Cup 99, CICIDS 2017, and NSL\u2010KDD, experimental evaluation proves that DeepNIDS not only outperforms other deep learning baseline models and traditional models with a detection rate of 96.8%, but also does well in terms of false alarm rate. Furthermore, DeepNIDS exhibits strong computational efficiency and resilience, making it appropriate for processing massive network traffic in real time. According to the report, deep learning technology offers fresh concepts for creating effective NIDS and is anticipated to play a significant role in network security protection in the future.<\/jats:p>","DOI":"10.1002\/spy2.70133","type":"journal-article","created":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T07:26:15Z","timestamp":1762932375000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Design and Implementation of Network Intrusion Detection System (\n                    <scp>NIDS<\/scp>\n                    ) Based on Deep Learning"],"prefix":"10.1002","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0775-7636","authenticated-orcid":false,"given":"Wenzhi","family":"Wang","sequence":"first","affiliation":[{"name":"School of Distance Education Jiaozuo University  Jiaozuo Henan China"}]},{"given":"Zhanqiao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Distance Education Jiaozuo University  Jiaozuo Henan China"}]}],"member":"311","published-online":{"date-parts":[[2025,11,11]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1134\/S0361768824700221"},{"issue":"10","key":"e_1_2_9_3_1","first-page":"5159","article-title":"Enhanced Network Intrusion Detection Using Deep Convolutional Neural Networks","volume":"12","author":"Naseer S.","year":"2018","journal-title":"KSII Transactions on Internet and Information 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