{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T09:54:35Z","timestamp":1774605275347,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T00:00:00Z","timestamp":1623888000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T00:00:00Z","timestamp":1623888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2021,10]]},"DOI":"10.1007\/s11063-021-10534-6","type":"journal-article","created":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T19:02:50Z","timestamp":1623956570000},"page":"3497-3523","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["CSCNN: Cost-Sensitive Convolutional Neural Network for Encrypted Traffic Classification"],"prefix":"10.1007","volume":"53","author":[{"given":"Shiva","family":"Soleymanpour","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4728-8278","authenticated-orcid":false,"given":"Hossein","family":"Sadr","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3897-0208","authenticated-orcid":false,"given":"Mojdeh","family":"Nazari Soleimandarabi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,17]]},"reference":[{"issue":"3","key":"10534_CR1","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 MJ, Zade RSH, Saberian M (2020) Deep packet: A novel approach for encrypted traffic classification using deep learning. Soft Comput 24(3):1999\u20132012","journal-title":"Soft Comput"},{"key":"10534_CR2","doi-asserted-by":"publisher","first-page":"54024","DOI":"10.1109\/ACCESS.2019.2912896","volume":"7","author":"P Wang","year":"2019","unstructured":"Wang P, Chen X, Ye F, Sun Z (2019) A survey of techniques for mobile service encrypted traffic classification using deep learning. IEEE Access 7:54024\u201354033","journal-title":"IEEE Access"},{"key":"10534_CR3","doi-asserted-by":"publisher","first-page":"102890","DOI":"10.1016\/j.jnca.2020.102890","volume":"173","author":"G D\u2019Angelo","year":"2021","unstructured":"D\u2019Angelo G, Palmieri F (2021) Network traffic classification using deep convolutional recurrent autoencoder neural networks for spatial\u2013temporal features extraction. J Netw Comput Appl 173:102890","journal-title":"J Netw Comput Appl"},{"key":"10534_CR4","doi-asserted-by":"crossref","unstructured":"Aceto G, Ciuonzo D, Montieri A, Pescap\u00e9 A (2021) DISTILLER: Encrypted traffic classification via multimodal multitask deep learning. J Netw Comput Appl:102985","DOI":"10.1016\/j.jnca.2021.102985"},{"key":"10534_CR5","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.comnet.2019.04.004","volume":"158","author":"KL Dias","year":"2019","unstructured":"Dias KL, Pongelupe MA, Caminhas WM, de Errico L (2019) An innovative approach for real-time network traffic classification. Comput Netw 158:143\u2013157","journal-title":"Comput Netw"},{"key":"10534_CR6","doi-asserted-by":"crossref","unstructured":"Soleymanpour S, Sadr H, Beheshti H An Efficient Deep Learning Method for Encrypted Traffic Classification on the Web. In: 2020 6th International Conference on Web Research (ICWR) (2020) IEEE, pp\u00a0209\u2013216","DOI":"10.1109\/ICWR49608.2020.9122299"},{"issue":"4","key":"10534_CR7","first-page":"13","volume":"8","author":"H Sadr","year":"2017","unstructured":"Sadr H, Nazari Solimandarabi M, Mirhosseini Moghadam M (2017) Categorization of persian detached handwritten letters using intelligent combinations of classifiers. J Adv Comput Res 8(4):13\u201321","journal-title":"J Adv Comput Res"},{"key":"10534_CR8","doi-asserted-by":"publisher","DOI":"10.22044\/jadm.2021.9618.2100","author":"H Sadr","year":"2021","unstructured":"Sadr H, Pedram MM, Teshnehlab M (2021) Convolutional neural network equipped with attention mechanism and transfer learning for enhancing performance of sentiment analysis. J AI Data Mining. https:\/\/doi.org\/10.22044\/jadm.2021.9618.2100","journal-title":"J AI Data Mining"},{"key":"10534_CR9","doi-asserted-by":"crossref","unstructured":"Sadr H, Soleimandarabi MN, Pedram M, Teshnelab M Unified Topic-Based Semantic Models: A Study in Computing the Semantic Relatedness of Geographic Terms. In: 2019 5th International Conference on Web Research (ICWR) (2019) IEEE, pp\u00a0134\u2013140","DOI":"10.1109\/ICWR.2019.8765257"},{"key":"10534_CR10","doi-asserted-by":"crossref","unstructured":"H\u00f6chst J, Baumg\u00e4rtner L, Hollick M, Freisleben B Unsupervised traffic flow classification using a neural autoencoder. In (2017) IEEE 42nd Conference on Local Computer Networks (LCN), 2017. IEEE, pp\u00a0523\u2013526","DOI":"10.1109\/LCN.2017.57"},{"key":"10534_CR11","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.neucom.2021.01.038","volume":"436","author":"Q Bi","year":"2021","unstructured":"Bi Q, Zhang H, Qin K (2021) Multi-scale stacking attention pooling for remote sensing scene classification. Neurocomput 436:147\u2013161","journal-title":"Neurocomput"},{"key":"10534_CR12","doi-asserted-by":"crossref","unstructured":"Wang Q, Huang W, Xiong Z, Li X (2020) Looking Closer at the Scene: Multiscale Representation Learning for Remote Sensing Image Scene Classification. IEEE Transactions on Neural Networks and Learning Systems","DOI":"10.1109\/TNNLS.2020.3042276"},{"issue":"5","key":"10534_CR13","first-page":"495","volume":"8","author":"AH Jadidinejad","year":"2015","unstructured":"Jadidinejad AH, Sadr H (2015) Improving weak queries using local cluster analysis as a preliminary framework. Indian J Sci Technol 8(5):495\u2013510","journal-title":"Indian J Sci Technol"},{"issue":"2","key":"10534_CR14","first-page":"1","volume":"10","author":"H Sadr","year":"2019","unstructured":"Sadr H, Nazari Solimandarabi M (2019) Presentation of an efficient automatic short answer grading model based on combination of pseudo relevance feedback and semantic relatedness measures. J Adv Comput Res 10(2):1\u201310","journal-title":"J Adv Comput Res"},{"key":"10534_CR15","doi-asserted-by":"crossref","unstructured":"Sadr H, Pedram MM, Teshnehlab M (2019) A Robust Sentiment Analysis Method Based on Sequential Combination of Convolutional and Recursive Neural Networks. Neural Process Lett:1\u201317","DOI":"10.1007\/s11063-019-10049-1"},{"issue":"2","key":"10534_CR16","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1109\/TGRS.2018.2864987","volume":"57","author":"Q Wang","year":"2018","unstructured":"Wang Q, Liu S, Chanussot J, Li X (2018) Scene classification with recurrent attention of VHR remote sensing images. IEEE Trans Geosci Remote Sens 57(2):1155\u20131167","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"10534_CR17","doi-asserted-by":"crossref","unstructured":"Draper-Gil G, Lashkari AH, Mamun MSI, Ghorbani AA Characterization of encrypted and vpn traffic using time-related. In: Proceedings of the 2nd international conference on information systems security and privacy (ICISSP) (2016) pp\u00a0407\u2013414","DOI":"10.5220\/0005740704070414"},{"issue":"3","key":"10534_CR18","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1109\/TNSM.2019.2933358","volume":"16","author":"A D\u2019Alconzo","year":"2019","unstructured":"D\u2019Alconzo A, Drago I, Morichetta A, Mellia M, Casas P (2019) A survey on big data for network traffic monitoring and analysis. IEEE Trans Netw Serv Manage 16(3):800\u2013813","journal-title":"IEEE Trans Netw Serv Manage"},{"key":"10534_CR19","doi-asserted-by":"crossref","unstructured":"Qi Y, Xu L, Yang B, Xue Y, Li J Packet classification algorithms: From theory to practice. In: IEEE INFOCOM 2009, 2009. IEEE, pp\u00a0648\u2013656","DOI":"10.1109\/INFCOM.2009.5061972"},{"issue":"1","key":"10534_CR20","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1109\/MNET.2012.6135854","volume":"26","author":"A Dainotti","year":"2012","unstructured":"Dainotti A, Pescape A, Claffy KC (2012) Issues and future directions in traffic classification. IEEE Network 26(1):35\u201340","journal-title":"IEEE Netw"},{"key":"10534_CR21","unstructured":"Madhukar A, Williamson C A longitudinal study of P2P traffic classification. In: 14th IEEE International Symposium on Modeling, Analysis, and Simulation (2006) IEEE, pp\u00a0179\u2013188"},{"key":"10534_CR22","doi-asserted-by":"crossref","unstructured":"Moore AW, Papagiannaki K Toward the accurate identification of network applications. In: International Workshop on Passive and Active Network Measurement (2005) Springer, pp\u00a041\u201354","DOI":"10.1007\/978-3-540-31966-5_4"},{"key":"10534_CR23","doi-asserted-by":"crossref","unstructured":"Sherry J, Lan C, Popa RA, Ratnasamy S, Blindbox: Deep packet inspection over encrypted traffic. In: Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication, 2015. pp\u00a0213\u2013226","DOI":"10.1145\/2785956.2787502"},{"key":"10534_CR24","doi-asserted-by":"crossref","unstructured":"Hua N, Song H, Lakshman T Variable-stride multi-pattern matching for scalable deep packet inspection. In: IEEE INFOCOM 2009, 2009. IEEE, pp\u00a0415\u2013423","DOI":"10.1109\/INFCOM.2009.5061946"},{"key":"10534_CR25","doi-asserted-by":"crossref","unstructured":"Wang X, Jiang J, Tang Y, Liu B, Wang X, StriD\u00b2FA: Scalable Regular Expression Matching for Deep Packet Inspection. In: 2011 IEEE International Conference on Communications (ICC) (2011) IEEE, pp\u00a01\u20135","DOI":"10.1109\/icc.2011.5963289"},{"issue":"27","key":"10534_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.17485\/ijst\/2015\/v8i27\/60811","volume":"8","author":"MN Soleimandarabi","year":"2015","unstructured":"Soleimandarabi MN, Mirroshandel SA (2015) A novel approach for computing semantic relatedness of geographic terms. Indian J Sci Technol 8(27):1\u201311","journal-title":"Indian J Sci Technol"},{"key":"10534_CR27","doi-asserted-by":"crossref","unstructured":"Piskac P, Novotny J Using of time characteristics in data flow for traffic classification. In: IFIP International Conference on Autonomous Infrastructure, Management and Security (2011) Springer, pp\u00a0173\u2013176","DOI":"10.1007\/978-3-642-21484-4_21"},{"key":"10534_CR28","doi-asserted-by":"crossref","unstructured":"Yildirim T, Radcliffe P VoIP traffic classification in IPSec tunnels. In: 2010 International Conference on Electronics and Information Engineering, 2010. IEEE, pp V1-151-V151-157","DOI":"10.1109\/ICEIE.2010.5559900"},{"issue":"1","key":"10534_CR29","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1145\/1198255.1198257","volume":"37","author":"M Crotti","year":"2007","unstructured":"Crotti M, Dusi M, Gringoli F, Salgarelli L (2007) Traffic classification through simple statistical fingerprinting. ACM SIGCOMM Comput Commun Rev 37(1):5\u201316","journal-title":"ACM SIGCOMM Comput Commun Rev"},{"key":"10534_CR30","doi-asserted-by":"crossref","unstructured":"Wang X, Parish DJ Optimised multi-stage tcp traffic classifier based on packet size distributions. In: 2010 Third International Conference on Communication Theory, Reliability, and Quality of Service, 2010. IEEE, pp\u00a098\u2013103","DOI":"10.1109\/CTRQ.2010.24"},{"issue":"1","key":"10534_CR31","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1109\/TNN.2006.883010","volume":"18","author":"T Auld","year":"2007","unstructured":"Auld T, Moore AW, Gull SF (2007) Bayesian neural networks for internet traffic classification. IEEE Trans Neural Netw 18(1):223\u2013239","journal-title":"IEEE Trans Neural Netw"},{"key":"10534_CR32","doi-asserted-by":"crossref","unstructured":"Sun R, Yang B, Peng L, Chen Z, Zhang L, Jing S Traffic classification using probabilistic neural networks. In: 2010 Sixth International Conference on Natural Computation, 2010. IEEE, pp\u00a01914\u20131919","DOI":"10.1109\/ICNC.2010.5584648"},{"key":"10534_CR33","doi-asserted-by":"crossref","unstructured":"Yamansavascilar B, Guvensan MA, Yavuz AG, Karsligil ME Application identification via network traffic classification. In: 2017 International Conference on Computing, Networking and Communications (ICNC) (2017) IEEE, pp\u00a0843\u2013848","DOI":"10.1109\/ICCNC.2017.7876241"},{"key":"10534_CR34","doi-asserted-by":"crossref","unstructured":"Chen Z, He K, Li J, Geng Y Seq2Img: A sequence-to-image based approach towards IP traffic classification using convolutional neural networks. In (2017) IEEE International Conference on Big Data (Big Data), 2017. IEEE, pp\u00a01271\u20131276","DOI":"10.1109\/BigData.2017.8258054"},{"key":"10534_CR35","doi-asserted-by":"publisher","first-page":"1792","DOI":"10.1109\/ACCESS.2017.2780250","volume":"6","author":"W Wang","year":"2017","unstructured":"Wang W, Sheng Y, Wang J, Zeng X, Ye X, Huang Y, Zhu M (2017) HAST-IDS: Learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection. IEEE Access 6:1792\u20131806","journal-title":"IEEE Access"},{"issue":"10","key":"10534_CR36","doi-asserted-by":"publisher","first-page":"2633","DOI":"10.1109\/TCSVT.2017.2703920","volume":"28","author":"Q Wang","year":"2017","unstructured":"Wang Q, Wan J, Yuan Y (2017) Deep metric learning for crowdedness regression. IEEE Trans Circuits Syst Video Technol 28(10):2633\u20132643","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"10534_CR37","doi-asserted-by":"publisher","first-page":"55380","DOI":"10.1109\/ACCESS.2018.2872430","volume":"6","author":"P Wang","year":"2018","unstructured":"Wang P, Ye F, Chen X, Qian Y (2018) Datanet: Deep learning based encrypted network traffic classification in sdn home gateway. IEEE Access 6:55380\u201355391","journal-title":"IEEE Access"},{"key":"10534_CR38","doi-asserted-by":"publisher","first-page":"18042","DOI":"10.1109\/ACCESS.2017.2747560","volume":"5","author":"M Lopez-Martin","year":"2017","unstructured":"Lopez-Martin M, Carro B, Sanchez-Esguevillas A, Lloret J (2017) Network traffic classifier with convolutional and recurrent neural networks for Internet of Things. IEEE Access 5:18042\u201318050","journal-title":"IEEE Access"},{"key":"10534_CR39","doi-asserted-by":"crossref","unstructured":"Wang W, Zhu M, Wang J, Zeng X, Yang Z End-to-end encrypted traffic classification with one-dimensional convolution neural networks. In (2017) IEEE International Conference on Intelligence and Security Informatics (ISI), 2017. IEEE, pp\u00a043\u201348","DOI":"10.1109\/ISI.2017.8004872"},{"key":"10534_CR40","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1016\/j.asoc.2013.08.014","volume":"14","author":"B Krawczyk","year":"2014","unstructured":"Krawczyk B, Wo\u017aniak M, Schaefer G (2014) Cost-sensitive decision tree ensembles for effective imbalanced classification. Appl Soft Comput 14:554\u2013562","journal-title":"Appl Soft Comput"},{"key":"10534_CR41","unstructured":"Chung Y-A, Lin H-T, Yang S-W (2015) Cost-aware pre-training for multiclass cost-sensitive deep learning. arXiv preprint arXiv:151109337"},{"key":"10534_CR42","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","volume":"106","author":"M Buda","year":"2018","unstructured":"Buda M, Maki A, Mazurowski MA (2018) A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw 106:249\u2013259","journal-title":"Neural Netw"},{"key":"10534_CR43","doi-asserted-by":"crossref","unstructured":"Wang S, Liu W, Wu J, Cao L, Meng Q, Kennedy PJ Training deep neural networks on imbalanced data sets. In (2016) international joint conference on neural networks (IJCNN), 2016. IEEE, pp\u00a04368\u20134374","DOI":"10.1109\/IJCNN.2016.7727770"},{"issue":"8","key":"10534_CR44","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.1109\/TNNLS.2017.2732482","volume":"29","author":"SH Khan","year":"2017","unstructured":"Khan SH, Hayat M, Bennamoun M, Sohel FA, Togneri R (2017) Cost-sensitive learning of deep feature representations from imbalanced data. IEEE Trans Neural Netw Learn syst 29(8):3573\u20133587","journal-title":"IEEE Trans Neural Netw Learn syst"},{"key":"10534_CR45","doi-asserted-by":"crossref","unstructured":"Telikani A, Gandomi AH (2019) Cost-sensitive stacked auto-encoders for intrusion detection in the Internet of Things. Internet of Things:100122","DOI":"10.1016\/j.iot.2019.100122"},{"key":"10534_CR46","unstructured":"Sadr H, Solimandarabi MN, Pedram MM, Teshnehlab M (2021) A Novel Deep Learning Method for Textual Sentiment Analysis. arXiv preprint arXiv:210211651"},{"key":"10534_CR47","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.patcog.2017.03.030","volume":"75","author":"Q Wang","year":"2018","unstructured":"Wang Q, Wan J, Yuan Y (2018) Locality constraint distance metric learning for traffic congestion detection. Pattern Recogn 75:272\u2013281","journal-title":"Pattern Recogn"},{"key":"10534_CR48","doi-asserted-by":"publisher","first-page":"86984","DOI":"10.1109\/ACCESS.2020.2992063","volume":"8","author":"H Sadr","year":"2020","unstructured":"Sadr H, Pedram MM, Teshnehlab M (2020) Multi-View Deep Network: A Deep Model Based on Learning Features From Heterogeneous Neural Networks for Sentiment Analysis. IEEE Access 8:86984\u201386997","journal-title":"IEEE Access"},{"issue":"3","key":"10534_CR49","first-page":"57","volume":"11","author":"H Sadr","year":"2019","unstructured":"Sadr H, Pedram MM, Teshnelab M (2019) Improving the performance of text sentiment analysis using deep convolutional neural Network Integrated with Hierarchical attention layer. Int J Inf Commun Technol Res 11(3):57\u201367","journal-title":"Int J Inf Commun Technol Res"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-021-10534-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-021-10534-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-021-10534-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T01:55:25Z","timestamp":1725242125000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-021-10534-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,17]]},"references-count":49,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,10]]}},"alternative-id":["10534"],"URL":"https:\/\/doi.org\/10.1007\/s11063-021-10534-6","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,17]]},"assertion":[{"value":"18 May 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}