{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T12:22:38Z","timestamp":1783513358240,"version":"3.55.0"},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100006469","name":"Fundo para o Desenvolvimento das Ci\u00eancias e da Tecnologia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006469","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":[[2026,8]]},"DOI":"10.1016\/j.comnet.2026.112499","type":"journal-article","created":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T06:22:05Z","timestamp":1782109325000},"page":"112499","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["EMLF-ETD: An efficient multi-level feature representation approach for encrypted traffic detection in variable-length traffic sessions"],"prefix":"10.1016","volume":"286","author":[{"given":"Zijie","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8972-8222","authenticated-orcid":false,"given":"Long","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6116-9154","authenticated-orcid":false,"given":"Hailin","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3675-9801","authenticated-orcid":false,"given":"Chenyang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4811-5141","authenticated-orcid":false,"given":"Binbin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6768-1483","authenticated-orcid":false,"given":"Jianqing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2229-0793","authenticated-orcid":false,"given":"Yuanyuan","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.comnet.2026.112499_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.iot.2023.100936","article-title":"Enhancing IoT network security through deep learning-powered intrusion detection system","volume":"24","author":"Bakhsh","year":"2023","journal-title":"Internet Things"},{"key":"10.1016\/j.comnet.2026.112499_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.iot.2025.101602","article-title":"Invisible eyes: Real-time activity detection through encrypted wi-fi traffic without machine learning","volume":"31","author":"Rasool","year":"2025","journal-title":"Internet Things"},{"key":"10.1016\/j.comnet.2026.112499_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.iot.2025.101599","article-title":"DI4IoT: A comprehensive framework for IoT device-type identification through network flow analysis","volume":"31","author":"Kumar","year":"2025","journal-title":"Internet Things"},{"key":"10.1016\/j.comnet.2026.112499_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.jnca.2025.104109","article-title":"STARNeT: Multidimensional spatial\u2013temporal attention recall network for accurate encrypted traffic classification","volume":"236","author":"Guan","year":"2025","journal-title":"J. Netw. Comput. Appl."},{"key":"10.1016\/j.comnet.2026.112499_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.jnca.2020.102871","article-title":"Distributed real-time SlowDoS attacks detection over encrypted traffic using artificial intelligence","volume":"173","author":"Garcia","year":"2021","journal-title":"J. Netw. Comput. Appl."},{"key":"10.1016\/j.comnet.2026.112499_b6","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.neucom.2022.03.007","article-title":"ABL-TC: A lightweight design for network traffic classification empowered by deep learning","volume":"489","author":"Wei","year":"2022","journal-title":"Neurocomputing"},{"issue":"6","key":"10.1016\/j.comnet.2026.112499_b7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3457904","article-title":"A survey on encrypted network traffic analysis applications, techniques, and countermeasures","volume":"54","author":"Papadogiannaki","year":"2021","journal-title":"ACM Comput. Surv."},{"issue":"10","key":"10.1016\/j.comnet.2026.112499_b8","doi-asserted-by":"crossref","first-page":"8416","DOI":"10.1109\/JIOT.2022.3228816","article-title":"Bota: Explainable iot malware detection in large networks","volume":"10","author":"Uh\u0159\u00ed\u010dek","year":"2022","journal-title":"IEEE Internet Things J."},{"issue":"4","key":"10.1016\/j.comnet.2026.112499_b9","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1007\/s10922-024-09847-3","article-title":"Enhanced malicious traffic detection in encrypted communication using TLS features and a multi-class classifier ensemble","volume":"32","author":"Kondaiah","year":"2024","journal-title":"J. Netw. Syst. Manage."},{"issue":"3","key":"10.1016\/j.comnet.2026.112499_b10","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1007\/s00500-019-04030-2","article-title":"Deep packet: A novel approach for encrypted traffic classification using deep learning","volume":"24","author":"Lotfollahi","year":"2020","journal-title":"Soft Comput."},{"key":"10.1016\/j.comnet.2026.112499_b11","series-title":"2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application","first-page":"267","article-title":"PEAN: A packet-level end-to-end attentive network for encrypted traffic identification","author":"Lin","year":"2021"},{"key":"10.1016\/j.comnet.2026.112499_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2024.110598","article-title":"Encrypted malicious traffic detection based on natural language processing and deep learning","author":"Zang","year":"2024","journal-title":"Comput. Netw."},{"key":"10.1016\/j.comnet.2026.112499_b13","series-title":"IEEE INFOCOM 2019-IEEE Conference on Computer Communications","first-page":"1171","article-title":"Fs-net: A flow sequence network for encrypted traffic classification","author":"Liu","year":"2019"},{"key":"10.1016\/j.comnet.2026.112499_b14","series-title":"2021 IFIP\/IEEE International Symposium on Integrated Network Management","first-page":"376","article-title":"Encrypted network traffic classification using a geometric learning model","author":"Huoh","year":"2021"},{"issue":"3","key":"10.1016\/j.comnet.2026.112499_b15","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.dcan.2021.09.009","article-title":"Length matters: Scalable fast encrypted internet traffic service classification based on multiple protocol data unit length sequence with composite deep learning","volume":"8","author":"Chen","year":"2022","journal-title":"Digit. Commun. Netw."},{"issue":"2","key":"10.1016\/j.comnet.2026.112499_b16","doi-asserted-by":"crossref","first-page":"2071","DOI":"10.1109\/TNSM.2022.3211254","article-title":"R1dit: Privacy-preserving malware traffic classification with attention-based neural networks","volume":"20","author":"Barut","year":"2022","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"10.1016\/j.comnet.2026.112499_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2021.108267","article-title":"Self-attentive deep learning method for online traffic classification and its interpretability","volume":"196","author":"Xie","year":"2021","journal-title":"Comput. Netw."},{"key":"10.1016\/j.comnet.2026.112499_b18","article-title":"OCEAN: Optional capability-based en route acknowledgement in network layer","author":"Yao","year":"2025","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"10.1016\/j.comnet.2026.112499_b19","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.eswa.2019.01.064","article-title":"Feature analysis of encrypted malicious traffic","volume":"125","author":"Shekhawat","year":"2019","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"10.1016\/j.comnet.2026.112499_b20","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1109\/COMST.2022.3208196","article-title":"Machine learning-powered encrypted network traffic analysis: A comprehensive survey","volume":"25","author":"Shen","year":"2022","journal-title":"IEEE Commun. Surv. Tutor."},{"issue":"4","key":"10.1016\/j.comnet.2026.112499_b21","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/MNET.011.1900366","article-title":"Optimizing feature selection for efficient encrypted traffic classification: A systematic approach","volume":"34","author":"Shen","year":"2020","journal-title":"IEEE Netw."},{"key":"10.1016\/j.comnet.2026.112499_b22","series-title":"2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics","first-page":"1","article-title":"Detection of encrypted and malicious network traffic using deep learning","author":"Reddy","year":"2023"},{"issue":"10","key":"10.1016\/j.comnet.2026.112499_b23","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1145\/3559439","article-title":"Traffic classification in an increasingly encrypted web","volume":"65","author":"Akbari","year":"2022","journal-title":"Commun. ACM"},{"key":"10.1016\/j.comnet.2026.112499_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2023.119229","article-title":"Graph based encrypted malicious traffic detection with hybrid analysis of multi-view features","volume":"644","author":"Hong","year":"2023","journal-title":"Inform. Sci."},{"issue":"2","key":"10.1016\/j.comnet.2026.112499_b25","doi-asserted-by":"crossref","first-page":"1224","DOI":"10.1109\/TNSM.2022.3227500","article-title":"Flow-based encrypted network traffic classification with graph neural networks","volume":"20","author":"Huoh","year":"2022","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"10.1016\/j.comnet.2026.112499_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2023.103143","article-title":"Feature mining for encrypted malicious traffic detection with deep learning and other machine learning algorithms","volume":"128","author":"Wang","year":"2023","journal-title":"Comput. Secur."},{"key":"10.1016\/j.comnet.2026.112499_b27","article-title":"ERNN: Error-resilient RNN for encrypted traffic detection towards network-induced phenomena","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"10.1016\/j.comnet.2026.112499_b28","series-title":"2017 International Conference on Information Networking","first-page":"712","article-title":"Malware traffic classification using convolutional neural network for representation learning","author":"Wang","year":"2017"},{"key":"10.1016\/j.comnet.2026.112499_b29","first-page":"1","article-title":"Towards real-time network intrusion detection with image-based sequential packets representation","author":"Ghadermazi","year":"2024","journal-title":"IEEE Trans. Big Data"},{"key":"10.1016\/j.comnet.2026.112499_b30","doi-asserted-by":"crossref","unstructured":"X. Lin, G. Xiong, G. Gou, Z. Li, J. Shi, J. Yu, Et-bert: A contextualized datagram representation with pre-training transformers for encrypted traffic classification, in: Proceedings of the ACM Web Conference 2022, 2022, pp. 633\u2013642.","DOI":"10.1145\/3485447.3512217"},{"key":"10.1016\/j.comnet.2026.112499_b31","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2021.108117","article-title":"PBCNN: Packet bytes-based convolutional neural network for network intrusion detection","volume":"194","author":"Yu","year":"2021","journal-title":"Comput. Netw."},{"key":"10.1016\/j.comnet.2026.112499_b32","series-title":"2016 IEEE European Symposium on Security and Privacy","first-page":"439","article-title":"Appscanner: Automatic fingerprinting of smartphone apps from encrypted network traffic","author":"Taylor","year":"2016"},{"key":"10.1016\/j.comnet.2026.112499_b33","doi-asserted-by":"crossref","first-page":"2367","DOI":"10.1109\/TIFS.2021.3050608","article-title":"Accurate decentralized application identification via encrypted traffic analysis using graph neural networks","volume":"16","author":"Shen","year":"2021","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.comnet.2026.112499_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2024.104134","article-title":"A graph representation framework for encrypted network traffic classification","volume":"148","author":"Okonkwo","year":"2025","journal-title":"Comput. Secur."},{"key":"10.1016\/j.comnet.2026.112499_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2024.110372","article-title":"DE-GNN: Dual embedding with graph neural network for fine-grained encrypted traffic classification","volume":"245","author":"Han","year":"2024","journal-title":"Comput. Netw."},{"key":"10.1016\/j.comnet.2026.112499_b36","series-title":"How powerful are graph neural networks?","author":"Xu","year":"2018"},{"key":"10.1016\/j.comnet.2026.112499_b37","series-title":"Encrypted mobile instant messaging traffic dataset","author":"Erdenebaatar","year":"2023"},{"key":"10.1016\/j.comnet.2026.112499_b38","series-title":"Proceedings of the 2nd International Conference on Information Systems Security and Privacy","first-page":"407","article-title":"Characterization of encrypted and VPN traffic using time-related features","author":"Gil","year":"2016"},{"key":"10.1016\/j.comnet.2026.112499_b39","doi-asserted-by":"crossref","unstructured":"H. Zhang, L. Yu, X. Xiao, Q. Li, F. Mercaldo, X. Luo, Q. Liu, Tfe-gnn: A temporal fusion encoder using graph neural networks for fine-grained encrypted traffic classification, in: Proceedings of the ACM Web Conference 2023, 2023, pp. 2066\u20132075.","DOI":"10.1145\/3543507.3583227"},{"key":"10.1016\/j.comnet.2026.112499_b40","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2024.109406","article-title":"A novel and effective encrypted traffic classification method based on channel attention and deformable convolution","volume":"118","author":"Zou","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.comnet.2026.112499_b41","article-title":"ConViTML: a convolutional vision transformer-based meta-learning framework for real-time edge network traffic classification","author":"Yang","year":"2024","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"10.1016\/j.comnet.2026.112499_b42","article-title":"MTSecurity: Privacy-preserving malicious traffic classification using graph neural network and transformer","author":"Yang","year":"2024","journal-title":"IEEE Trans. Netw. Serv. Manag."}],"container-title":["Computer Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1389128626005116?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1389128626005116?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T11:55:47Z","timestamp":1783511747000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1389128626005116"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":42,"alternative-id":["S1389128626005116"],"URL":"https:\/\/doi.org\/10.1016\/j.comnet.2026.112499","relation":{},"ISSN":["1389-1286"],"issn-type":[{"value":"1389-1286","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"EMLF-ETD: An efficient multi-level feature representation approach for encrypted traffic detection in variable-length traffic sessions","name":"articletitle","label":"Article Title"},{"value":"Computer Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.comnet.2026.112499","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"112499"}}