{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T12:29:19Z","timestamp":1764332959332,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030620042"},{"type":"electronic","value":"9783030620059"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-62005-9_31","type":"book-chapter","created":{"date-parts":[[2020,10,17]],"date-time":"2020-10-17T11:02:34Z","timestamp":1602932554000},"page":"430-444","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["NSA-Net: A NetFlow Sequence Attention Network for Virtual Private Network Traffic Detection"],"prefix":"10.1007","author":[{"given":"Peipei","family":"Fu","sequence":"first","affiliation":[]},{"given":"Chang","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Qingya","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Zhenzhen","family":"Li","sequence":"additional","affiliation":[]},{"given":"Gaopeng","family":"Gou","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Xiong","sequence":"additional","affiliation":[]},{"given":"Zhen","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,18]]},"reference":[{"key":"31_CR1","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1016\/B978-0-12-803843-7.00058-2","volume-title":"Computer and Information Security Handbook","author":"JT Harmening","year":"2017","unstructured":"Harmening, J.T.: Virtual private networks. In: Vacca, J.R. (ed.) Computer and Information Security Handbook, pp. 843\u2013856. Morgan Kaufmann, Burlington (2017)"},{"issue":"3","key":"31_CR2","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.1007\/s00500-019-04030-2","volume":"24","author":"M Lotfollahi","year":"2020","unstructured":"Lotfollahi, M., Siavoshani, M.J., et al.: Deep packet: a novel approach for encrypted traffic classification using deep learning. Soft Comput. 24(3), 1999\u20132012 (2020). https:\/\/doi.org\/10.1007\/s00500-019-04030-2","journal-title":"Soft Comput."},{"key":"31_CR3","doi-asserted-by":"crossref","unstructured":"Zain ul Abideen, M., Saleem, S., Ejaz, M.: VPN traffic detection in SSL-protected channel. Secur. Commun. Netw. 2019(5), 1\u201317 (2019)","DOI":"10.1155\/2019\/7924690"},{"key":"31_CR4","doi-asserted-by":"crossref","unstructured":"Draper-Gil, G., Lashkari, A.H., Mamun, M.S.I., et al.: Characterization of encrypted and vpn traffic using time-related. In: ICISSP, pp. 407\u2013414 (2016)","DOI":"10.5220\/0005740704070414"},{"issue":"2","key":"31_CR5","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1080\/23742917.2017.1321891","volume":"1","author":"S Bagui","year":"2017","unstructured":"Bagui, S., Fang, X., et al.: Comparison of machine-learning algorithms for classification of VPN network traffic flow using time-related features. J. Cyber Secur. Technol. 1(2), 108\u2013126 (2017)","journal-title":"J. Cyber Secur. Technol."},{"key":"31_CR6","doi-asserted-by":"crossref","unstructured":"Wang,W., Zhu, M., Wang, J., et al.: End-to-end encrypted traffic classification with one-dimensional convolution neural networks. In: 2017 IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 43\u201348. IEEE (2017)","DOI":"10.1109\/ISI.2017.8004872"},{"key":"31_CR7","doi-asserted-by":"crossref","unstructured":"Miller, S., Curran, K., Lunney, T.: Multilayer perceptron neural network for detection of encrypted VPN network traffic. In: 2018 International Conference On Cyber Situational Awareness, Data Analytics And Assessment, pp. 1\u20138. IEEE (2018)","DOI":"10.1109\/CyberSA.2018.8551395"},{"issue":"1","key":"31_CR8","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/s11554-019-00930-6","volume":"17","author":"L Guo","year":"2020","unstructured":"Guo, L., Wu, Q., Liu, S., et al.: Deep learning-based real-time VPN encrypted traffic identification methods. J. Real-Time Image Proc. 17(1), 103\u2013114 (2020). https:\/\/doi.org\/10.1007\/s11554-019-00930-6","journal-title":"J. Real-Time Image Proc."},{"key":"31_CR9","doi-asserted-by":"crossref","unstructured":"Claise, B.: Cisco systems neflow services export version 9 (2004)","DOI":"10.17487\/rfc3954"},{"key":"31_CR10","doi-asserted-by":"crossref","unstructured":"Zhou, P., Shi, W., Tian, J., et al.: Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 207\u2013212 (2016)","DOI":"10.18653\/v1\/P16-2034"},{"issue":"5","key":"31_CR11","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1145\/2677046.2677050","volume":"44","author":"R Hofstede","year":"2014","unstructured":"Hofstede, R., Hendriks, L., Sperotto, A., et al.: SSH compromise detection using NetFlow\/IPFIX. ACM SIGCOMM Comput. Commun. Rev. 44(5), 20\u201326 (2014)","journal-title":"ACM SIGCOMM Comput. Commun. Rev."},{"key":"31_CR12","doi-asserted-by":"crossref","unstructured":"Schatzmann, D., M\u00fchlbauer, W., Spyropoulos, T., et al.: Digging into HTTPS: flow-based classification of webmail traffic. In: Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement, pp. 322-327 (2010)","DOI":"10.1145\/1879141.1879184"},{"key":"31_CR13","doi-asserted-by":"crossref","unstructured":"Manzoor, J., Drago, I., Sadre, R.: How HTTP\/2 is changing Web traffic and how to detect it. In: 2017 Network Traffic Measurement and Analysis Conference (TMA), pp. 1\u20139. IEEE (2017)","DOI":"10.23919\/TMA.2017.8002899"},{"key":"31_CR14","doi-asserted-by":"crossref","unstructured":"Lv, B., Yu, X., Xu, G., et al.: Network traffic monitoring system based on big data technology. In: Proceedings of the International Conference on Big Data and Computing 2018, pp. 27\u201332 (2018)","DOI":"10.1145\/3220199.3220221"},{"key":"31_CR15","doi-asserted-by":"crossref","unstructured":"Liu, X., Tang, Z., Yang, B.: Predicting network attacks with CNN by constructing images from NetFlow Data. In: BigDataSecurity, pp. 61\u201366. IEEE (2019)","DOI":"10.1109\/BigDataSecurity-HPSC-IDS.2019.00022"},{"key":"31_CR16","doi-asserted-by":"publisher","first-page":"7842","DOI":"10.1109\/ACCESS.2019.2963716","volume":"8","author":"CT Yang","year":"2020","unstructured":"Yang, C.T., Liu, J.C., Kristiani, E., et al.: NetFlow monitoring and cyberattack detection using deep learning with Ceph. IEEE Access 8, 7842\u20137850 (2020)","journal-title":"IEEE Access"},{"key":"31_CR17","unstructured":"Mnih, V., Heess, N., Graves A.: Recurrent models of visual attention. In: Advances in Neural Information Processing Systems, pp. 2204\u20132212 (2014)"},{"key":"31_CR18","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. Comput. Sci. arXiv preprint arXiv:1409.0473 (2014)"},{"issue":"4","key":"31_CR19","first-page":"429","volume":"10","author":"J Chorowski","year":"2015","unstructured":"Chorowski, J., Bahdanau, D., Serdyuk, D., et al.: Attention-based models for speech recognition. Comput. Sci. 10(4), 429\u2013439 (2015)","journal-title":"Comput. Sci."},{"key":"31_CR20","doi-asserted-by":"crossref","unstructured":"Luong, M.T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. Comput. Sci. arXiv preprint arXiv:1508.04025 (2015)","DOI":"10.18653\/v1\/D15-1166"},{"key":"31_CR21","unstructured":"Softflowd. http:\/\/www.mindrot.org\/projects\/softflowd\/"},{"key":"31_CR22","unstructured":"Nfdump. http:\/\/nfdump.sourceforge.net\/"},{"key":"31_CR23","unstructured":"Abadi, M., Agarwal, A., et al.: Tensor-flow: large-scale machine learning on heterogeneous distributed systems, arXiv preprint arXiv:1603.04467 (2016)"},{"key":"31_CR24","unstructured":"Chollet, F., et al.: Keras (2017). https:\/\/github.com\/fchollet\/keras"},{"key":"31_CR25","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR, vol. abs\/1412.6980 (2014)"}],"container-title":["Lecture Notes in Computer Science","Web Information Systems Engineering \u2013 WISE 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-62005-9_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,17]],"date-time":"2021-07-17T06:09:15Z","timestamp":1626502155000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-62005-9_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030620042","9783030620059"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-62005-9_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"18 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WISE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Web Information Systems Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Amsterdam","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wise2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/wasp.cs.vu.nl\/WISE2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}