{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T07:15:19Z","timestamp":1781334919844,"version":"3.54.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T00:00:00Z","timestamp":1689120000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T00:00:00Z","timestamp":1689120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100005311","name":"China Southern Power Grid","doi-asserted-by":"publisher","award":["ZDKJXM20170002"],"award-info":[{"award-number":["ZDKJXM20170002"]}],"id":[{"id":"10.13039\/501100005311","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004832","name":"Changsha University of Science and Technology","doi-asserted-by":"crossref","award":["6080201-000101204"],"award-info":[{"award-number":["6080201-000101204"]}],"id":[{"id":"10.13039\/501100004832","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100012540","name":"Guangdong Province Introduction of Innovative R&D Team","doi-asserted-by":"publisher","award":["2020YFB0906003"],"award-info":[{"award-number":["2020YFB0906003"]}],"id":[{"id":"10.13039\/100012540","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EURASIP J. on Info. Security"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Traffic classification is widely used in network security and network management. Early studies have mainly focused on mapping network traffic to different unencrypted applications, but little research has been done on network traffic classification of encrypted applications, especially the underlying traffic of encrypted applications. To address the above issues, this paper proposes a network encryption traffic classification model that combines attention mechanisms and spatiotemporal features. The model firstly uses the long short-term memory (LSTM) method to analyze continuous network flows and find the temporal correlation features between these network flows. Secondly, the convolutional neural network (CNN) method is used to extract the high-order spatial features of the network flow, and then, the squeeze and excitation (SE) module is used to weight and redistribute the high-order spatial features to obtain the key spatial features of the network flow. Finally, through the above three stages of training and learning, fast classification of network flows is achieved. The main advantages of this model are as follows: (1) the mapping relationship between network flow and label is automatically constructed by the model without manual intervention and decision by network features, (2) it has strong generalization ability and can quickly adapt to different network traffic datasets, and (3) it can handle encrypted applications and their underlying traffic with high accuracy. The experimental results show that the model can be applied to classify network traffic of encrypted and unencrypted applications at the same time, especially the classification accuracy of the underlying traffic of encrypted applications is improved. In most cases, the accuracy generally exceeds 90%.<\/jats:p>","DOI":"10.1186\/s13635-023-00141-4","type":"journal-article","created":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T23:19:07Z","timestamp":1689117547000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Network traffic classification model based on attention mechanism and spatiotemporal features"],"prefix":"10.1186","volume":"2023","author":[{"given":"Feifei","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Situo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xubin","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liu","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niandong","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanqi","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,12]]},"reference":[{"key":"141_CR1","first-page":"43","volume-title":"2017 IEEE International Conference on Intelligence and Security Informatics (ISI). End-to-end encrypted traffic classification with one-dimensional convolution neural networks","author":"W Wang","year":"2017","unstructured":"W. Wang, M. Zhu, J. Wang, X. Zeng, Z. Yang, in 2017 IEEE International Conference on Intelligence and Security Informatics (ISI). End-to-end encrypted traffic classification with one-dimensional convolution neural networks. (IEEE, Beijing, 2017), pp.43\u201348"},{"issue":"4","key":"141_CR2","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1109\/TNET.2014.2320577","volume":"23","author":"J Zhang","year":"2015","unstructured":"J. Zhang, X. Chen, Y. Xiang, W. Zhou, J. Wu, Robust network traffic classification. IEEE-ACM Transactions on Networking. 23(4), 1257\u20131270 (2015)","journal-title":"IEEE-ACM Transactions on Networking."},{"key":"141_CR3","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1016\/j.ins.2018.10.039","volume":"479","author":"X Xiao","year":"2019","unstructured":"X. Xiao, R. Li, H. Zheng, R. Ye, A. KumarSangaiah, S. Xia, Novel dynamic multiple classification system for network traffic. Inf. Sci. 479, 526\u2013541 (2019)","journal-title":"Inf. Sci."},{"issue":"2","key":"141_CR4","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.1109\/SURV.2013.100613.00161","volume":"16","author":"F Michael","year":"2014","unstructured":"F. Michael, R. Chris, R. Eduardo, M. JeanAlexander, H. Klaus, A survey of payload based traffic classification approaches. IEEE Communications Surveys and Tutorials. 16(2), 1135\u20131156 (2014)","journal-title":"IEEE Communications Surveys and Tutorials."},{"issue":"5","key":"141_CR5","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/MCOM.2019.1800819","volume":"57","author":"S Rezaei","year":"2019","unstructured":"S. Rezaei, X. Liu, Deep learning for encrypted traffic classification: an overview. IEEE Commun. Mag. 57(5), 76\u201381 (2019)","journal-title":"IEEE Commun. Mag."},{"issue":"3","key":"141_CR6","first-page":"51","volume":"5","author":"Y Soundharya","year":"2014","unstructured":"Y. Soundharya, A. Bhanu Prasad, Network traffic classification with Naive Bayes predictions. Int. J. Coal. Sci. Technol. 5(3), 51\u2013254 (2014)","journal-title":"Int. J. Coal. Sci. Technol."},{"key":"141_CR7","first-page":"046","volume-title":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). Packet-based network traffic classification using deep learning","author":"H-K Lim","year":"2019","unstructured":"H.-K. Lim, J. -B. Kim, J. -S. Heo, K. Kim, Y.-G. Hong, Y.-H. Han, in 2019 International Conference on Artificial\nIntelligence in Information and Communication (ICAIIC). Packet-based\nnetwork traffic classification using deep learning. (Okinawa, Japan, 2019), pp.046\u2013051"},{"issue":"8","key":"141_CR8","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Y. Bengio, A. Courville, P. Vincent, Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798\u20131828 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"141_CR9","first-page":"712","volume-title":"2017 International Conference on Information Networking (ICOIN). Malware traffic classification using convolutional neural network for representation learning","author":"W Wang","year":"2017","unstructured":"W. Wang, M. Zhu, X. Zeng, X. Ye, Y. Sheng, in 2017 International Conference on Information Networking (ICOIN). Malware traffic classification using convolutional neural network for representation learning (Da Nang, Vietnam, 2017), pp. 712\u2013717."},{"key":"141_CR10","first-page":"407","volume-title":"Proceedings of the 2nd international conference on information systems security and privacy . Characterization of encrypted and VPN traffic using timerelated features","author":"G DraperGil","year":"2016","unstructured":"G. DraperGil, A.H. Lashkari, M.S.I. Mamun, A.A. Ghorbani, in Proceedings of the 2nd international conference on information systems security and privacy . Characterization of encrypted and VPN traffic using timerelated features. (London, the United Kingdom, 2016), pp.407\u2013414"},{"key":"141_CR11","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.1007\/s00500-019-04030-2","volume":"24","author":"M Lotfollahi","year":"2020","unstructured":"M. Lotfollahi, M. Jafari Siavoshani, R. Shirali Hossein Zade et al., Deep packet: a novel approach for encrypted traffic classification using deep learning. Soft. Computing. 24, 1999\u20132012 (2020)","journal-title":"Soft Computing."},{"key":"141_CR12","unstructured":"Qosmos. Deep packet inspection and metadata engine, [online]. https:\/\/www.qosmos.com\/products\/deep-packet-inspection-engine\/.\u00a0Accessed 17 Mar 2021"},{"key":"141_CR13","unstructured":"Paloalto. Paloalto networks, [online]. https:\/\/www.paloaltonetworks.com\/.\u00a0Accessed 20 Feb 2021"},{"key":"141_CR14","volume-title":"Proceedings of the 2010 ACM Conference on Emerging Networking Experiments and Technology. Internet traffic classification demystified: on the sources of the discriminative power","author":"Y Lim","year":"2010","unstructured":"Y. Lim, H. Kim, J. Jeong, C. Kim, Y. Choi, in Proceedings of the 2010 ACM Conference on Emerging Networking Experiments and Technology. Internet traffic classification demystified: on the sources of the discriminative power. (Philadelphia, PA, USA, 2010)"},{"issue":"1","key":"141_CR15","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.comnet.2011.09.007","volume":"56","author":"C Lu","year":"2012","unstructured":"C. Lu et al., Session level flow classification by packet size distribution and session grouping. Comput. Netw. 56(1), 260\u2013272 (2012)","journal-title":"Comput. Netw."},{"key":"141_CR16","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.comnet.2014.11.001","volume":"76","author":"T Bujlow","year":"2015","unstructured":"T. Bujlow, V. Carela-Espanol, P. Barlet-Ros, Independent comparison of popular dpi tools for traffic classification. Comput. Netw. 76, 75\u201389 (2015)","journal-title":"Comput. Netw."},{"key":"141_CR17","unstructured":"Sarah Perez. Eff: Half of web traffic is now encrypted, [online]. https:\/\/techcrunch.com\/2017\/02\/22\/eff-half-the-web-is-now-encrypted\/. Accessed 20 Mar 2021"},{"issue":"S2","key":"141_CR18","first-page":"245","volume":"37","author":"Y Hu","year":"2020","unstructured":"Y. Hu, H. Jin, C. Wang, Network flow association method based on compressed sensing. Computer. Appl. Res. 37(S2), 245\u2013246+ 241 (2020). (in Chinese)","journal-title":"Computer application research."},{"key":"141_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2019.101681","volume":"89","author":"J Zhang","year":"2020","unstructured":"J. Zhang, Y. Ling et al., Model of the intrusion detection system based on the integration of spatial-temporal features. Comput. Secur. 89, 101681 (2020)","journal-title":"Comput. Secur."},{"key":"141_CR20","volume-title":"The IEEE International Conference on Computer Vision (ICCV) . Learning multi-attention convolutional neural network for fine-grained image recognition","author":"H Zheng","year":"2017","unstructured":"H. Zheng, J. Fu, T. Mei, J. Luo, in The IEEE International Conference on Computer Vision (ICCV) . Learning multi-attention convolutional neural network for fine-grained image recognition. (Venice, Italy, 2017)"},{"issue":"1","key":"141_CR21","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1093\/nsr\/nwx110","volume":"005","author":"H Li","year":"2018","unstructured":"H. Li, Deep learning for natural language processing: advantages and challenges. Natl. Sci. Rev. 005(1), 24\u201326 (2018)","journal-title":"Natl. Sci. Rev."},{"issue":"3","key":"141_CR22","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1109\/MCI.2018.2840738","volume":"13","author":"Y Tom Young","year":"2018","unstructured":"Y. Tom Young, H. Devamanyu, P. Soujanya, C. Erik, Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag.\u00a013(3), 55\u201375 (2018)","journal-title":"IEEE Comput. Intell Mag"},{"issue":"5","key":"141_CR23","doi-asserted-by":"publisher","first-page":"644","DOI":"10.3390\/sym11050644","volume":"11","author":"D Wang","year":"2019","unstructured":"D. Wang, X. Wang, S. Lv, End-to-end mandarin speech recognition combining CNN and BLSTM. Symmetry 11(5), 644 (2019)","journal-title":"Symmetry"},{"key":"141_CR24","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1016\/j.bspc.2018.08.035","volume":"47","author":"J Zhao","year":"2019","unstructured":"J. Zhao, X. Mao, L. Chen, Speech emotion recognition using deep 1d and 2d CNN LSTM networks. Biomed. Signal Process. Control 47, 312\u2013323 (2019)","journal-title":"Biomed. Signal Process. Control"},{"key":"141_CR25","first-page":"1","volume-title":"2018 IEEE\/ACM 26th International Symposium on Quality of Service (IWQoS). Byte segment neural network for network traffic classification","author":"R Li","year":"2018","unstructured":"R. Li, X. Xiao, in 2018 IEEE\/ACM 26th International Symposium on Quality of Service (IWQoS). Byte segment neural network for network traffic classification. (Banff, Canada, 2018), pp.1\u201310"},{"key":"141_CR26","first-page":"680","volume-title":"IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). FlowPic: encrypted Internet traffic classification is as easy as image recognition","author":"T Shapira","year":"2019","unstructured":"T. Shapira, Y. Shavitt, in IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). FlowPic: encrypted Internet traffic classification is as easy as image recognition. (Pairs, France, 2019), pp.680\u2013687"},{"issue":"4","key":"141_CR27","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.eij.2022.06.006","volume":"23","author":"A Almomani","year":"2022","unstructured":"A. Almomani, Classification of virtual private networks encrypted traffic using ensemble learning algorithms. Egyptian Informatics Journal 23(4), 57\u201368 (2022)","journal-title":"Egyptian Informatics Journal"},{"key":"141_CR28","first-page":"1","volume-title":"Communications Quality and Reliability (CQR), 2011 IEEE International Workshop Technical Committee . Application identification from encrypted traffic based on characteristic changes by encryption","author":"Y Okada","year":"2011","unstructured":"Y. Okada, S. Ata, N. Nakamura, Y. Nakahira, I. Oka, in Communications Quality and Reliability (CQR), 2011 IEEE International Workshop Technical Committee. Application identification from encrypted traffic based on characteristic changes by encryption. (Naples, Italy, 2011), pp.1\u20136"},{"issue":"7","key":"141_CR29","first-page":"107","volume":"57","author":"J Wei","year":"2021","unstructured":"J. Wei, R. Zheng, J. Liu, Research on malicious TLS traffic identification based on hybrid neural network. Comput. Eng. Appl. 57(7), 107\u2013114 (2021). (in Chinese)","journal-title":"Comput. Eng. Appl."},{"key":"141_CR30","first-page":"781","volume-title":"Proceedings of the 33rd IEEE Annual Conference on Computer Communications (IEEE INFOCOM). Markov chain fingerprinting to classify encrypted traffic","author":"M Korczynski","year":"2014","unstructured":"M. Korczynski, A. Duda, in Proceedings of the 33rd IEEE Annual Conference on Computer Communications (IEEE INFOCOM). Markov chain fingerprinting to classify encrypted traffic. (Toronto, Canada, 2014), pp.781\u2013789"},{"issue":"5","key":"141_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/nem.1981","volume":"27","author":"K Shim","year":"2017","unstructured":"K. Shim, J. Ham, B.D. Sija et al., Application traffic classification using payload size sequence signature. Int. J. Network Manage 27(5), 1\u201317 (2017)","journal-title":"Int. J. Network Manage"},{"issue":"7","key":"141_CR32","first-page":"2261","volume":"8","author":"JH Ham","year":"2014","unstructured":"J.H. Ham, H.M. An, M.S. Kim, Application traffic classification using PSS signature. KSII Transactions on Internet and Information Systems (TIIS). 8(7), 2261\u20132280 (2014)","journal-title":"KSII Transactions on Internet and Information Systems (TIIS)."},{"issue":"3","key":"141_CR33","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.1007\/s00500-019-04030-2","volume":"24","author":"M Lotfollahi","year":"2020","unstructured":"M. Lotfollahi, M. Jafari Siavoshani, R. Shirali Hossein Zade, M. Saberian, Deep packet: a novel approach for encrypted traffic classification using deep learning. Soft Comput. 24(3), 1999\u20132012 (2020)","journal-title":"Soft Comput."},{"key":"141_CR34","first-page":"43","volume-title":"CAN 2017 - Proceedings of the 2017 Cloud-Assisted Networking Workshop. DeepFlow: a deep learning framework for software-defined measurement","author":"A Lazaris","year":"2017","unstructured":"A. Lazaris, V.K. Prasanna, in CAN 2017 - Proceedings of the 2017 Cloud-Assisted Networking Workshop. DeepFlow: a deep learning framework for software-defined measurement. (Vilanova i la Geltr\u00fa, Barcelona, Spain, 2017), pp.43\u201348"},{"issue":"11","key":"141_CR35","first-page":"4246","volume":"14","author":"JW Li","year":"2020","unstructured":"J.W. Li, Z.S. Pan, Network traffic classification based on deep learning. KSII Transact Internet Inform Syst. 14(11), 4246\u20134267 (2020)","journal-title":"KSII Transact Internet Inform Syst."},{"key":"141_CR36","unstructured":"KDD-CUP99 dataset, [online]. https:\/\/archive.ics.uci.edu\/ml\/datasets\/KDD+Cup+1999+Data.\u00a0Accessed 23 Dec 2020"},{"key":"141_CR37","unstructured":"NSL-KDD dataset, [online]. https:\/\/www.unb.ca\/cic\/datasets\/nsl.html.\u00a0Accessed 20 Feb 2021"},{"key":"141_CR38","unstructured":"UNSW_NB15 dataset, [online]. https:\/\/ieee-dataport.org\/documents\/unswnb15-dataset.\u00a0Accessed 8 June 2021"},{"key":"141_CR39","first-page":"247","volume-title":"2014 Second International Conference on Advanced Cloud and Big Data. An intrusion detection model based on deep belief nets","author":"N Gao","year":"2014","unstructured":"N. Gao, L. Gao, Q. Gao, H. Wang, in 2014 Second International Conference on Advanced Cloud and Big Data. An intrusion detection model based on deep belief nets. (Huangshan, China, 2014), pp.247\u2013252"},{"issue":"1","key":"141_CR40","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/TETCI.2017.2772792","volume":"2","author":"N Shone","year":"2018","unstructured":"N. Shone, T.N. Ngoc, V.D. Phai, Q. Shi, A deep learning approach to network intrusion detection. IEEE Transact. Emerging Topics Comput Intell. 2(1), 41\u201350 (2018)","journal-title":"IEEE Transact. Emerging Topics Comput Intell."},{"key":"141_CR41","unstructured":"VPN-nonVPN dataset (ISCXVPN2016), [online]. https:\/\/www.unb.ca\/cic\/datasets\/vpn.html.\u00a0Accessed 18 May 2021"},{"key":"141_CR42","unstructured":"USTC-TFC2016 dataset, [online]. https:\/\/github.com\/yungshenglu\/USTC-TFC2016.\u00a0Accessed 8 July 2020"},{"key":"141_CR43","unstructured":"YouTube dataset, [online]. http:\/\/www.cse.bgu.ac.il\/title_fingerprinting\/.\u00a0Accessed 21 Mar 2021"},{"key":"141_CR44","unstructured":"SplitCap tool, [online]. https:\/\/www.netresec.com\/?page=SplitCap.\u00a0Accessed 13 June 2022"},{"key":"141_CR45","unstructured":"IDX file format, [online]. https:\/\/fon.hum.uva.nl\/praat\/manual\/IDX_file_format.htm.\u00a0Accessed 3 Sept 2021"},{"issue":"8","key":"141_CR46","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"S. Hochreiter, J. Schmidhuber, Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"141_CR47","first-page":"1","volume-title":"The 3rd International Conference on Learning Representations (ICLR2015) .Very deep convolutional networks for large-scale image recognition","author":"K Simonyan","year":"2015","unstructured":"K. Simonyan, A. Zisserman, in The 3rd International Conference on Learning Representations (ICLR2015) .Very deep convolutional networks for large-scale image recognition. (San Diego, CA, 2015), pp.1\u201314"},{"key":"141_CR48","first-page":"7132","volume-title":"the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Squeeze-and-excitation networks","author":"J Hu","year":"2018","unstructured":"J. Hu, L. She, G. Sun, in the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Squeeze-and-excitation networks. (Salt Lake City, USA, 2018), pp.7132\u20137141"},{"issue":"12","key":"141_CR49","doi-asserted-by":"publisher","first-page":"3039","DOI":"10.1109\/TIFS.2017.2730819","volume":"12","author":"R Dubin","year":"2017","unstructured":"R. Dubin et al., I know what you saw last minute - encrypted HTTP adaptive video streaming title classification. IEEE Transact Inform Forensics Secur. 12(12), 3039\u20133049 (2017)","journal-title":"IEEE Transact Inform Forensics Secur."}],"container-title":["EURASIP Journal on Information Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13635-023-00141-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13635-023-00141-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13635-023-00141-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T23:20:35Z","timestamp":1689117635000},"score":1,"resource":{"primary":{"URL":"https:\/\/jis-eurasipjournals.springeropen.com\/articles\/10.1186\/s13635-023-00141-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,12]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["141"],"URL":"https:\/\/doi.org\/10.1186\/s13635-023-00141-4","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-353938\/v1","asserted-by":"object"}]},"ISSN":["2510-523X"],"issn-type":[{"value":"2510-523X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,12]]},"assertion":[{"value":"22 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 July 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"6"}}