{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T17:26:47Z","timestamp":1783099607715,"version":"3.54.6"},"reference-count":43,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T00:00:00Z","timestamp":1602720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61433012"],"award-info":[{"award-number":["61433012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovation Environment Construction Special Project of Xinjiang Uygur Autonomous Region","award":["PT1811"],"award-info":[{"award-number":["PT1811"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Intrusion detection system (IDS) plays a significant role in preventing network attacks and plays a vital role in the field of national security. At present, the existing intrusion detection methods are generally based on traditional machine learning models, such as random forest and decision tree, but they rely heavily on artificial feature extraction and have relatively low accuracy. To solve the problems of feature extraction and low detection accuracy in intrusion detection, an intrusion detection model SAAE-DNN, based on stacked autoencoder (SAE), attention mechanism and deep neural network (DNN), is proposed. The SAE represents data with a latent layer, and the attention mechanism enables the network to obtain the key features of intrusion detection. The trained SAAE encoder can not only automatically extract features, but also initialize the weights of DNN potential layers to improve the detection accuracy of DNN. We evaluate the performance of SAAE-DNN in binary-classification and multi-classification on an NSL-KDD dataset. The SAAE-DNN model can detect normally and attack symmetrically, with an accuracy of 87.74% and 82.14% (binary-classification and multi-classification), which is higher than that of machine learning methods such as random forest and decision tree. The experimental results show that the model has a better performance than other comparison methods.<\/jats:p>","DOI":"10.3390\/sym12101695","type":"journal-article","created":{"date-parts":[[2020,10,17]],"date-time":"2020-10-17T07:23:22Z","timestamp":1602919402000},"page":"1695","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":88,"title":["SAAE-DNN: Deep Learning Method on Intrusion Detection"],"prefix":"10.3390","volume":"12","author":[{"given":"Chaofei","family":"Tang","sequence":"first","affiliation":[{"name":"College of Software, Xinjiang University, Urumqi 830000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nurbol","family":"Luktarhan","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi 830000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxin","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Software, Xinjiang University, Urumqi 830000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"67542","DOI":"10.1109\/ACCESS.2020.2983568","article-title":"An Intrusion Detection Model With Hierarchical Attention Mechanism","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s12065-019-00293-8","article-title":"Implementation of adaptive scheme in evolutionary technique for anomaly-based intrusion detection","volume":"13","author":"Dwivedi","year":"2020","journal-title":"Evol. 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