{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T00:38:24Z","timestamp":1705106304511},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684802","type":"print"},{"value":"9781643684819","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,12]]},"abstract":"<jats:p>Deep learning of network traffic is a research method that mimics the structural functions of the human nervous system to identify, classify, and predict data. We propose a new model based on Conv-LSTM to improve the accuracy and efficiency of network encrypted traffic recognition. Based on the public CIC-ISD2017 dataset, the new model is tested and measured, and evaluated based on the constructed confusion matrix and ROC graph. Comparing it with traditional Conv-LSTM, decision tree method, and RF&amp;LSTM methods, it was found that the new model performs better and can perform well in multi classification tasks, with an accuracy rate of up to 99.60%. This model provides a reference solution for relevant applications in the field of network security.<\/jats:p>","DOI":"10.3233\/faia231192","type":"book-chapter","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T12:55:42Z","timestamp":1705064142000},"source":"Crossref","is-referenced-by-count":0,"title":["Network Traffic Recognition and Classification Based on Deep Learning"],"prefix":"10.3233","author":[{"given":"Zhihao","family":"Song","sequence":"first","affiliation":[{"name":"School of Medical Information and Engineering, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai\u2019an, 271016, China"}]},{"given":"Lanhua","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Medical Information and Engineering, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai\u2019an, 271016, China"}]},{"given":"Jin","family":"Wang","sequence":"additional","affiliation":[{"name":"Cybersecurity Industry Development Center (Information Center) of Ministry of Industry and Information Technology, Beijing, 100846, China"}]},{"given":"Xiaoyan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Medical Information and Engineering, Shandong First Medical University and Shandong Academy of Medical Sciences, Tai\u2019an, 271016, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Electronics, Communications and Networks"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA231192","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T12:55:44Z","timestamp":1705064144000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA231192"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,12]]},"ISBN":["9781643684802","9781643684819"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia231192","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,12]]}}}