{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:24:32Z","timestamp":1773246272975,"version":"3.50.1"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031840999","type":"print"},{"value":"9783031841002","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\/978-3-031-84100-2_41","type":"book-chapter","created":{"date-parts":[[2025,3,8]],"date-time":"2025-03-08T00:00:40Z","timestamp":1741392040000},"page":"347-354","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Tiny Neural Networks for\u00a0Session-Level Traffic Classification"],"prefix":"10.1007","author":[{"given":"Adel","family":"Chehade","sequence":"first","affiliation":[]},{"given":"Edoardo","family":"Ragusa","sequence":"additional","affiliation":[]},{"given":"Paolo","family":"Gastaldo","sequence":"additional","affiliation":[]},{"given":"Rodolfo","family":"Zunino","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,8]]},"reference":[{"key":"41_CR1","doi-asserted-by":"crossref","unstructured":"Mehdi Seydali, Farshad Khunjush, Behzad Akbari, and Javad Dogani. Cbs: A deep learning approach for encrypted traffic classification with mixed spatio-temporal and statistical features. IEEE Access, 2023.","DOI":"10.2139\/ssrn.4189457"},{"key":"41_CR2","unstructured":"Cisco. Cisco 2018 annual cybersecurity report. Technical report, Cisco, 2018."},{"key":"41_CR3","doi-asserted-by":"crossref","unstructured":"Mohammad Lotfollahi, Mahdi Jafari\u00a0Siavoshani, Ramin Shirali Hossein\u00a0Zade, and Mohammdsadegh Saberian. Deep packet: A novel approach for encrypted traffic classification using deep learning. Soft Computing, 24(3):1999\u20132012, 2020.","DOI":"10.1007\/s00500-019-04030-2"},{"key":"41_CR4","doi-asserted-by":"crossref","unstructured":"Mudassar Hussain, Nadir Shah, Rashid Amin, Sultan\u00a0S Alshamrani, Aziz Alotaibi, and Syed\u00a0Mohsan Raza. Software-defined networking: Categories, analysis, and future directions. Sensors, 22(15):5551, 2022.","DOI":"10.3390\/s22155551"},{"key":"41_CR5","doi-asserted-by":"crossref","unstructured":"Gerard Draper-Gil, Arash\u00a0Habibi Lashkari, Mohammad Saiful\u00a0Islam Mamun, and Ali\u00a0A Ghorbani. Characterization of encrypted and vpn traffic using time-related. In Proceedings of the 2nd international conference on information systems security and privacy (ICISSP), pages 407\u2013414, 2016.","DOI":"10.5220\/0005740704070414"},{"key":"41_CR6","doi-asserted-by":"crossref","unstructured":"Swapnil\u00a0Sayan Saha, Sandeep\u00a0Singh Sandha, and Mani Srivastava. Machine learning for microcontroller-class hardware: A review. IEEE Sensors Journal, 22(22):21362\u201321390, 2022.","DOI":"10.1109\/JSEN.2022.3210773"},{"key":"41_CR7","doi-asserted-by":"publisher","first-page":"61135","DOI":"10.1109\/ACCESS.2022.3181135","volume":"10","author":"Muhammad Sameer Sheikh and Yinqiao Peng","year":"2022","unstructured":"Muhammad\u00a0Sameer Sheikh and Yinqiao Peng. Procedures, criteria, and machine learning techniques for network traffic classification: a survey. IEEE Access, 10:61135\u201361158, 2022.","journal-title":"IEEE Access"},{"key":"41_CR8","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.inffus.2021.02.009","volume":"72","author":"J Zhao","year":"2021","unstructured":"Jingjing Zhao, Xuyang Jing, Zheng Yan, and Witold Pedrycz. Network traffic classification for data fusion: A survey. Information Fusion, 72:22\u201347, 2021.","journal-title":"Information Fusion"},{"key":"41_CR9","doi-asserted-by":"crossref","unstructured":"Wei Wang, Ming Zhu, Jinlin Wang, Xuewen Zeng, and Zhongzhen Yang. End-to-end encrypted traffic classification with one-dimensional convolution neural networks. In 2017 IEEE international conference on intelligence and security informatics (ISI), pages 43\u201348. IEEE, 2017.","DOI":"10.1109\/ISI.2017.8004872"},{"key":"41_CR10","doi-asserted-by":"crossref","unstructured":"Yanjie He and Wei Li. Image-based encrypted traffic classification with convolution neural networks. In 2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC), pages 271\u2013278. IEEE, 2020.","DOI":"10.1109\/DSC50466.2020.00048"},{"issue":"6","key":"41_CR11","doi-asserted-by":"publisher","first-page":"1080","DOI":"10.3390\/sym13061080","volume":"13","author":"L Bei","year":"2021","unstructured":"Bei Lu, Nurbol Luktarhan, Chao Ding, and Wenhui Zhang. Iclstm: encrypted traffic service identification based on inception-lstm neural network. Symmetry, 13(6):1080, 2021.","journal-title":"Symmetry"},{"key":"41_CR12","doi-asserted-by":"crossref","unstructured":"Shiva Soleymanpour, Hossein Sadr, and Mojdeh Nazari\u00a0Soleimandarabi. Cscnn: cost-sensitive convolutional neural network for encrypted traffic classification. Neural Processing Letters, 53(5):3497\u20133523, 2021.","DOI":"10.1007\/s11063-021-10534-6"},{"key":"41_CR13","doi-asserted-by":"crossref","unstructured":"Susu Cui, Bo\u00a0Jiang, Zhenzhen Cai, Zhigang Lu, Song Liu, and Jian Liu. A session-packets-based encrypted traffic classification using capsule neural networks. In 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC\/SmartCity\/DSS), pages 429\u2013436. IEEE, 2019.","DOI":"10.1109\/HPCC\/SmartCity\/DSS.2019.00071"},{"key":"41_CR14","unstructured":"Colin White, Mahmoud Safari, Rhea Sukthanker, Binxin Ru, Thomas Elsken, Arber Zela, Debadeepta Dey, and Frank Hutter. Neural architecture search: Insights from 1000 papers. arXiv preprint arXiv:2301.08727, 2023."},{"key":"41_CR15","doi-asserted-by":"crossref","unstructured":"Edoardo Ragusa, Federica Zonzini, Paolo Gastaldo, and Luca De\u00a0Marchi. Combining compressed sensing and neural architecture search for sensor-near vibration diagnostics. IEEE Transactions on Industrial Informatics, 2024.","DOI":"10.1109\/TII.2024.3395648"},{"key":"41_CR16","doi-asserted-by":"crossref","unstructured":"Maonan Wang, Kangfeng Zheng, Dan Luo, Yanqing Yang, and Xiujuan Wang. An encrypted traffic classification framework based on convolutional neural networks and stacked autoencoders. In 2020 IEEE 6th International Conference on Computer and Communications (ICCC), pages 634\u2013641. IEEE, 2020.","DOI":"10.1109\/ICCC51575.2020.9344978"},{"key":"41_CR17","doi-asserted-by":"crossref","unstructured":"Mingze Song, Jing Ran, and Shulan Li. Encrypted traffic classification based on text convolution neural networks. In 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT), pages 432\u2013436. IEEE, 2019.","DOI":"10.1109\/ICCSNT47585.2019.8962493"}],"container-title":["Lecture Notes in Electrical Engineering","Applications in Electronics Pervading Industry, Environment and Society"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-84100-2_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,8]],"date-time":"2025-03-08T00:00:49Z","timestamp":1741392049000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-84100-2_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031840999","9783031841002"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-84100-2_41","relation":{},"ISSN":["1876-1100","1876-1119"],"issn-type":[{"value":"1876-1100","type":"print"},{"value":"1876-1119","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"8 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ApplePies","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applications in Electronics Pervading Industry, Environment and Society","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"applepies2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}