{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T07:45:44Z","timestamp":1763451944401,"version":"3.45.0"},"reference-count":50,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T00:00:00Z","timestamp":1760313600000},"content-version":"vor","delay-in-days":0,"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>Aiming to address the problems of low time efficiency and poor generalization ability in support vector machine (SVM) models when dealing with large\u2010scale network intrusions, this paper suggests a large\u2010scale robust intrusion detection model that combines deep neural network (DNN) and multi\u2010kernel approximate SVM. The DNN conducts representation learning to extract intrinsic features and performs dimensionality reduction on the dataset. The paper leverages the capability of multi\u2010kernel learning to accommodate the distinct characteristics of various features within the input space. Additionally, it aims to further improve the robustness of the model. The multi\u2010kernel approximation SVM using random Fourier features to perform kernel approximation can handle large\u2010scale datasets. The model employs the gradient descent method to train neural networks and multi\u2010kernel SVM from start to finish. The gradient descent algorithm can effectively maximize convergence toward the global minimum. This, in turn, enhances the overall accuracy of the model. Our model was tested on three intrusion detection datasets of varying scales: UNSW\u2010NB15, CIC\u2010IDS2017, and CIC\u2010IDS2018; and compared with the latest learning models such as gradient boosting tree, CNN, LSTM, GNN, transfer learning, and SVM models of different variants. The accuracy rate of the model proposed in this paper has increased by 1%\u20134% compared with that of the currently popular intrusion detection models. The experimental findings show that our model has higher classification performance and better robustness when processing large\u2010scale datasets while reducing time complexity.<\/jats:p>","DOI":"10.1002\/spy2.70117","type":"journal-article","created":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T04:13:22Z","timestamp":1760415202000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Intrusion Detection Model Based on Multi\u2010Kernel Approximation and Deep Neural Network"],"prefix":"10.1002","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1846-8616","authenticated-orcid":false,"given":"Yukun","family":"Wu","sequence":"first","affiliation":[{"name":"Hangzhou Vocational &amp; Technical College  Hangzhou China"},{"name":"Zhejiang Anteng Information Technology Co., Ltd  Shaoxing China"}]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[{"name":"Zhejiang Anteng Information Technology Co., Ltd  Shaoxing China"}]},{"given":"Yunzhi","family":"Chen","sequence":"additional","affiliation":[{"name":"Hangzhou Vocational &amp; Technical College  Hangzhou China"}]},{"given":"Wei William","family":"Lee","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology Zhejiang University of Technology  Hangzhou 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