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As an effective means of network protection, anomaly network traffic detection can detect various known attacks. However, there are also some shortcomings. Deep learning brings a new opportunity for the further development of anomaly network traffic detection. So far, the existing deep learning models cannot fully learn the temporal and spatial features of network traffic and their classification accuracy needs to be improved. To fill this gap, this paper proposes an anomaly network traffic detection model integrating temporal and spatial features (ITSN) using a three-layer parallel network structure. ITSN learns the temporal and spatial features of the traffic and fully fuses these two features through feature fusion technology to improve the accuracy of network traffic classification. On this basis, an improved method of raw traffic feature extraction is proposed, which can reduce redundant features, speed up the convergence of the network, and ease the imbalance of the datasets. The experimental results on the ISCX-IDS 2012 and CICIDS 2017 datasets show that the ITSN can improve the accuracy of anomaly network traffic detection while enhancing the robustness of the detection system and has a higher recognition rate for positive samples.<\/jats:p>","DOI":"10.1155\/2021\/7045823","type":"journal-article","created":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T17:27:05Z","timestamp":1629739625000},"page":"1-15","source":"Crossref","is-referenced-by-count":18,"title":["Design and Implementation of an Anomaly Network Traffic Detection Model Integrating Temporal and Spatial Features"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5290-7446","authenticated-orcid":true,"given":"Ming","family":"Li","sequence":"first","affiliation":[{"name":"College of Information, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8861-5461","authenticated-orcid":true,"given":"Dezhi","family":"Han","sequence":"additional","affiliation":[{"name":"College of Information, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2078-3335","authenticated-orcid":true,"given":"Xinming","family":"Yin","sequence":"additional","affiliation":[{"name":"The Third Research Institute of the Ministry of Public Security, Shanghai 200031, China"},{"name":"Department of Computer Science and Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1085-9877","authenticated-orcid":true,"given":"Han","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1986-7144","authenticated-orcid":true,"given":"Dun","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2019.2927497"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2015.11.016"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/tvt.2020.3036631"},{"key":"4","volume-title":"Deep Learning for Network Traffic Classification and Anomaly Detection","author":"W. 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