{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T12:47:30Z","timestamp":1783946850932,"version":"3.55.0"},"reference-count":31,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100002855","name":"Ministry of Science and Technology of the People's Republic of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002855","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Networks"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1016\/j.comnet.2024.110403","type":"journal-article","created":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T15:43:56Z","timestamp":1712591036000},"page":"110403","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":18,"special_numbering":"C","title":["Combine intra- and inter-flow: A multimodal encrypted traffic classification model driven by diverse features"],"prefix":"10.1016","volume":"245","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0005-1372-3069","authenticated-orcid":false,"given":"Xiangbin","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-6598-8190","authenticated-orcid":false,"given":"Qingjun","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-9819-404X","authenticated-orcid":false,"given":"Yongjuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-3533-4874","authenticated-orcid":false,"given":"Gaopeng","family":"Gou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunxiang","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-3190-6521","authenticated-orcid":false,"given":"Gang","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.comnet.2024.110403_b1","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1007\/s10462-021-10037-9","article-title":"A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions","volume":"55","author":"Thakkar","year":"2021","journal-title":"Artif. Intell. Rev."},{"issue":"4","key":"10.1016\/j.comnet.2024.110403_b2","doi-asserted-by":"crossref","first-page":"2371","DOI":"10.1007\/s13369-019-03970-z","article-title":"Analysis of support vector machine-based intrusion detection techniques","volume":"45","author":"Bhati","year":"2020","journal-title":"Arab. J. Sci. Eng."},{"key":"10.1016\/j.comnet.2024.110403_b3","doi-asserted-by":"crossref","unstructured":"Thijs van Ede, Riccardo Bortolameotti, Andrea Continella, Jingjing Ren, Daniel J. Dubois, Martina Lindorfer, David R. Choffnes, Maarten van Steen, Andreas Peter, FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic, in: Proceedings 2020 Network and Distributed System Security Symposium, 2020, pp. 1\u201318.","DOI":"10.14722\/ndss.2020.24412"},{"key":"10.1016\/j.comnet.2024.110403_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2019.107042","article-title":"Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic","volume":"168","author":"Elmasry","year":"2020","journal-title":"Comput. Netw.","ISSN":"https:\/\/2.zoppoz.workers.dev:443\/https\/id.crossref.org\/issn\/1389-1286","issn-type":"print"},{"key":"10.1016\/j.comnet.2024.110403_b5","doi-asserted-by":"crossref","unstructured":"Zhe Wang, Baihe Ma, Yong Zeng, Xiaojie Lin, Kaichao Shi, Ziwen Wang, Differential Preserving in XGBoost Model for Encrypted Traffic Classification, in: International Conference on Networking and Network Applications, 2022, pp. 220\u2013225.","DOI":"10.1109\/NaNA56854.2022.00044"},{"key":"10.1016\/j.comnet.2024.110403_b6","doi-asserted-by":"crossref","unstructured":"Xin Wang, Shuhui Chen, Jinshu Su, App-Net: A Hybrid Neural Network for Encrypted Mobile Traffic Classification, in: IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, 2020, pp. 424\u2013429.","DOI":"10.1109\/INFOCOMWKSHPS50562.2020.9162891"},{"key":"10.1016\/j.comnet.2024.110403_b7","doi-asserted-by":"crossref","first-page":"106944.1","DOI":"10.1016\/j.comnet.2019.106944","article-title":"MIMETIC: Mobile encrypted traffic classification using multimodal deep learning","volume":"165","author":"Aceto","year":"2019","journal-title":"Comput. Netw."},{"key":"10.1016\/j.comnet.2024.110403_b8","doi-asserted-by":"crossref","unstructured":"Alec F. Diallo, Paul Patras, Adaptive Clustering-based Malicious Traffic Classification at the Network Edge, in: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, 2021, pp. 1\u201310.","DOI":"10.1109\/INFOCOM42981.2021.9488690"},{"key":"10.1016\/j.comnet.2024.110403_b9","doi-asserted-by":"crossref","unstructured":"Wei Wang, Ming Zhu, Jinlin Wang, Xuewen Zeng, Zhongzhen Yang, End-to-end encrypted traffic classification with one-dimensional convolution neural networks, in: IEEE International Conference on Intelligence and Security Informatics, 2017, pp. 43\u201348.","DOI":"10.1109\/ISI.2017.8004872"},{"issue":"10","key":"10.1016\/j.comnet.2024.110403_b10","first-page":"2207","article-title":"A novel method for encrypted traffic classification using N-gram-based techniques","volume":"12","author":"van Deventer","year":"2017","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.comnet.2024.110403_b11","series-title":"2017 IEEE Conference on Dependable and Secure Computing","first-page":"491","article-title":"Automated traffic classification and application identification using machine learning","author":"Khedr","year":"2017"},{"key":"10.1016\/j.comnet.2024.110403_b12","series-title":"IEEE Conference on Local Computer Networks","first-page":"226","article-title":"Encrypted traffic classification using machine learning techniques: A case study with netflix traffic","author":"van Deventer","year":"2016"},{"key":"10.1016\/j.comnet.2024.110403_b13","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1109\/TNET.2022.3216603","article-title":"ProGraph: Robust network traffic identification with graph propagation","volume":"31","author":"Li","year":"2023","journal-title":"IEEE\/ACM Trans. Netw."},{"issue":"1","key":"10.1016\/j.comnet.2024.110403_b14","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1109\/TPDS.2012.98","article-title":"Network traffic classification using correlation information","volume":"24","author":"Zhang","year":"2013","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"10.1016\/j.comnet.2024.110403_b15","doi-asserted-by":"crossref","unstructured":"Zhiju Yang, Weiping Pei, Mon-Chu Chen, Chuan Yue, WTAGRAPH: Web Tracking and Advertising Detection using Graph Neural Networks, in: IEEE Symposium on Security and Privacy, 2022, pp. 1540\u20131557.","DOI":"10.1109\/SP46214.2022.9833670"},{"key":"10.1016\/j.comnet.2024.110403_b16","doi-asserted-by":"crossref","unstructured":"Wenhao Li, Huaifeng Bao, Xiao-Yu Zhang, Lin Li, AMDetector: Detecting Large-Scale and Novel Android Malware Traffic with Meta-learning, in: International Conference on Conceptual Structures, 2022, pp. 387\u2013401.","DOI":"10.1007\/978-3-031-08760-8_33"},{"key":"10.1016\/j.comnet.2024.110403_b17","series-title":"International Conference on Conceptual Structures","first-page":"380","article-title":"Gblnet: Detecting intrusion traffic with multi-granularity bilstm","author":"Li","year":"2022"},{"key":"10.1016\/j.comnet.2024.110403_b18","doi-asserted-by":"crossref","first-page":"12113","DOI":"10.1109\/TPAMI.2023.3275156","article-title":"Multimodal learning with transformers: A survey","volume":"45","author":"Xu","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.comnet.2024.110403_b19","doi-asserted-by":"crossref","unstructured":"V.F. Taylor, R. Spolaor, M. Conti, I. Martinovic, AppScanner: Automatic fingerprinting of smartphone apps from encrypted network traffic, in: IEEE European Symposium on Security and Privacy, (EuroS&P), 2016, pp. 439\u2013454.","DOI":"10.1109\/EuroSP.2016.40"},{"issue":"3","key":"10.1016\/j.comnet.2024.110403_b20","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1109\/TNET.2014.2320577","article-title":"Robust network traffic classification","volume":"23","author":"Zhang","year":"2015","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"10.1016\/j.comnet.2024.110403_b21","doi-asserted-by":"crossref","unstructured":"C. Liu, L. He, G. Xiong, Z. Cao, Z. Li, FS-Net: A flow sequence network for encrypted traffic classification, in: IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, 2019, pp. 1171\u20131179.","DOI":"10.1109\/INFOCOM.2019.8737507"},{"issue":"8","key":"10.1016\/j.comnet.2024.110403_b22","doi-asserted-by":"crossref","first-page":"1830","DOI":"10.1109\/TIFS.2017.2692682","article-title":"Classification of encrypted traffic with second-order Markov chains and application attribute bigrams","volume":"12","author":"Shen","year":"2017","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.comnet.2024.110403_b23","series-title":"HPCC\/DSS\/SmartCity\/DependSys","first-page":"478","article-title":"MEMG: Mobile encrypted traffic classification with Markov chains and graph neural network","author":"Cai","year":"2021"},{"key":"10.1016\/j.comnet.2024.110403_b24","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1109\/TNET.2022.3215507","article-title":"A novel multimodal deep learning framework for encrypted traffic classification","volume":"31","author":"Lin","year":"2023","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"10.1016\/j.comnet.2024.110403_b25","doi-asserted-by":"crossref","unstructured":"Khalid Shahbar, Nur Zincir-Heywood, How far can we push flow analysis to identify encrypted anonymity network traffic?, in: NOMS 2018 - 2018 IEEE\/IFIP Network Operations and Management Symposium, 2018, pp. 1\u20136.","DOI":"10.1109\/NOMS.2018.8406156"},{"key":"10.1016\/j.comnet.2024.110403_b26","doi-asserted-by":"crossref","unstructured":"Riyad Alshammari, Nur Zincir-Heywood, Machine learning based encrypted traffic classification: Identifying SSH and Skype, in: 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, 2009, pp. 1\u20138.","DOI":"10.1109\/CISDA.2009.5356534"},{"key":"10.1016\/j.comnet.2024.110403_b27","doi-asserted-by":"crossref","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","article-title":"T-GCN: A temporal graph convolutional network for traffic prediction","volume":"21","author":"Zhao","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.comnet.2024.110403_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.105794","article-title":"GTFE-net: A gramian time frequency enhancement CNN for bearing fault diagnosis","volume":"119","author":"Jia","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"1.3","key":"10.1016\/j.comnet.2024.110403_b29","doi-asserted-by":"crossref","first-page":"87","DOI":"10.30534\/ijatcse\/2019\/1781.32019","article-title":"Statistical features-MLP neural network for recognizing bivariate spc chart patterns","volume":"8","author":"Masood","year":"2019","journal-title":"Int. J. Adv. Trends Comput. Sci. Eng."},{"key":"10.1016\/j.comnet.2024.110403_b30","doi-asserted-by":"crossref","unstructured":"Gerard Draper-Gil, Arash Habibi Lashkari, Mohammad Saiful Islam Mamun, Ali A. Ghorbani, Characterization of Encrypted and VPN Traffic using Time-related Features, in: International Conference on Information Systems Security and Privacy, 2016, pp. 312\u2013315.","DOI":"10.5220\/0005740704070414"},{"key":"10.1016\/j.comnet.2024.110403_b31","article-title":"Boau: Malicious traffic detection with noise labels based on boundary augmentation","volume":"131","author":"jun Yuan","year":"2023","journal-title":"Comput. Secur."}],"container-title":["Computer Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S1389128624002354?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S1389128624002354?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T22:08:51Z","timestamp":1731708531000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/linkinghub.elsevier.com\/retrieve\/pii\/S1389128624002354"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5]]},"references-count":31,"alternative-id":["S1389128624002354"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.comnet.2024.110403","relation":{},"ISSN":["1389-1286"],"issn-type":[{"value":"1389-1286","type":"print"}],"subject":[],"published":{"date-parts":[[2024,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Combine intra- and inter-flow: A multimodal encrypted traffic classification model driven by diverse features","name":"articletitle","label":"Article Title"},{"value":"Computer Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.comnet.2024.110403","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"110403"}}