{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T09:51:40Z","timestamp":1770976300565,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":19,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819698110","type":"print"},{"value":"9789819698127","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-9812-7_26","type":"book-chapter","created":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T07:27:33Z","timestamp":1753428453000},"page":"308-320","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MACL: A Masked Autoencoder Framework with Contrastive Learning for Efficient Encrypted Malicious Traffic Detection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7902-4985","authenticated-orcid":false,"given":"Teng","family":"Ren","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6177-0062","authenticated-orcid":false,"given":"Wenqi","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8437-5894","authenticated-orcid":false,"given":"Shunliang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0020-0926","authenticated-orcid":false,"given":"Jie","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,26]]},"reference":[{"key":"26_CR1","doi-asserted-by":"crossref","unstructured":"He, H.Y., Yang, Z.G., Chen, X.N.: Pert: payload encoding representation from transformer for encrypted traffic classification. In: ITU Kaleidoscope: Industry-Driven Digital Transformation (ITU K), pp. 1\u20138. IEEE (2020)","DOI":"10.23919\/ITUK50268.2020.9303204"},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"Lin, X., Xiong, G., Gou, G., Li, Z., Shi, J., Yu, J.: ET-BERT: a contextualized datagram representation with pre-training transformers for encrypted traffic classification. In: Proceedings of the ACM Web Conference 2022, pp. 633\u2013642 (2022)","DOI":"10.1145\/3485447.3512217"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Zhao, R., et al.: Yet another traffic classifier: a masked autoencoder based traffic transformer with multi-level flow representation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 5420\u20135427 (2023)","DOI":"10.1609\/aaai.v37i4.25674"},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the NAACL-HLT 2019, pp. 4171\u20134186 (2019)","DOI":"10.18653\/v1\/N19-1423"},{"key":"26_CR5","unstructured":"Dosovitskiy, A., et al.: An Image is Worth 16\u00a0\u00d7\u00a016 Words: Transformers for Image Recognition at Scale (2020). arXiv preprint arXiv:2010.11929"},{"key":"26_CR6","doi-asserted-by":"publisher","first-page":"128444","DOI":"10.1016\/j.neucom.2024.128444","volume":"617","author":"W Dong","year":"2025","unstructured":"Dong, W., Yu, J., Lin, X., Gou, G., Xiong, G.: Deep learning and pre-training technology for encrypted traffic classification: a comprehensive review. Neurocomputing 617, 128444 (2025). https:\/\/doi.org\/10.1016\/j.neucom.2024.128444","journal-title":"Neurocomputing"},{"key":"26_CR7","doi-asserted-by":"crossref","unstructured":"Taylor, V.F., Spolaor, R., Conti, M., Martinovic, I.: AppScanner: automatic fingerprinting of smartphone apps from encrypted network traffic. In: Proceedings of the IEEE EuroSP 2016, pp. 439\u2013454 (2016)","DOI":"10.1109\/EuroSP.2016.40"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Van Ede, T., et al.: FlowPrint: semi-supervised mobile-app fingerprinting on encrypted network traffic. In: Network and Distributed System Security Symposium (NDSS), vol. 27 (2020)","DOI":"10.14722\/ndss.2020.24412"},{"issue":"3","key":"26_CR9","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., Shirali Hossein Zade, R., Saberian, M.: 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":"26_CR10","doi-asserted-by":"crossref","unstructured":"Liu, C., He, L., Xiong, G., Cao, Z., Li, Z.: FS-Net: a flow sequence network for encrypted traffic classification. In: Proceedings of the IEEE INFOCOM 2019, pp. 1171\u20131179 (2019)","DOI":"10.1109\/INFOCOM.2019.8737507"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"Sirinam, P., Imani, M., Juarez, M., Wright, M.: Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep Learning (2018). arXiv preprint arXiv:1801.02265","DOI":"10.1145\/3243734.3243768"},{"key":"26_CR12","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A Simple Framework for Contrastive Learning of Visual Representations. In: Proceedings of the ICML, pp. 1597\u20131607. PMLR (2020)"},{"key":"26_CR13","unstructured":"Grill, J.B., et al.: Bootstrap your own latent: a new approach to self-supervised learning. In: Advances in Neural Information Processing Systems, vol. 33, pp. 21271\u201321284 (2020)"},{"key":"26_CR14","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"26_CR15","unstructured":"Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al.: Improving Language Understanding by Generative Pre-Training (2018)"},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"Beigi, E.B., Jazi, H.H., Stakhanova, N., Ghorbani, A.A.: Towards effective feature selection in machine learning-based botnet detection approaches. In: Proceedings of the IEEE CNS 2014, pp. 247\u2013255 (2014)","DOI":"10.1109\/CNS.2014.6997492"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Wang, W., Zhu, M., Zeng, X., Ye, X., Sheng, Y.: Malware traffic classification using convolutional neural network for representation learning. In: Proceedings of the ICOIN 2017, pp. 712\u2013717. IEEE (2017)","DOI":"10.1109\/ICOIN.2017.7899588"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Panchenko, A., Lanze, F., Pennekamp, J., Engel, T., Zinnen, A., Henze, M., Wehrle, K.: Website Fingerprinting at Internet Scale. In: NDSS, vol. 1, p. 23477 (2016)","DOI":"10.14722\/ndss.2016.23477"},{"key":"26_CR19","doi-asserted-by":"crossref","unstructured":"Hang, Z., Lu, Y., Wang, Y., Xie, Y.: Flow-MAE: leveraging masked autoencoder for accurate, efficient and robust malicious traffic classification. In: Proceedings of the 26th International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2023), pp. 297\u2013314. ACM, Hong Kong, China (2023)","DOI":"10.1145\/3607199.3607206"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-9812-7_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T08:57:39Z","timestamp":1770973059000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-9812-7_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819698110","9789819698127"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-9812-7_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"26 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","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":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}