{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T16:58:49Z","timestamp":1790960329429,"version":"4.1.0"},"reference-count":26,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Commun."],"published-print":{"date-parts":[[2024,1,1]]},"DOI":"10.1587\/transcom.2023wwp0004","type":"journal-article","created":{"date-parts":[[2023,10,16]],"date-time":"2023-10-16T22:27:21Z","timestamp":1697495241000},"page":"117-125","source":"Crossref","is-referenced-by-count":7,"title":["Device Type Classification Based on Two-Stage Traffic Behavior Analysis"],"prefix":"10.23919","volume":"E107.B","author":[{"given":"Chikako","family":"TAKASAKI","sequence":"first","affiliation":[{"name":"Network Service Systems Laboratories, NTT Corporation"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomohiro","family":"KORIKAWA","sequence":"additional","affiliation":[{"name":"Network Service Systems Laboratories, NTT Corporation"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyota","family":"HATTORI","sequence":"additional","affiliation":[{"name":"Network Service Systems Laboratories, NTT Corporation"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hidenari","family":"OHWADA","sequence":"additional","affiliation":[{"name":"Network Service Systems Laboratories, NTT Corporation"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] C. Takasaki, T. Korikawa, K. Hattori, and H. Ohwada, \u201cTraffic behavior-based device type classification,\u201d 2023 International Conference on Computing, Networking and Communications (ICNC), pp.353-357, 2023. 10.1109\/icnc57223.2023.10074041","DOI":"10.1109\/ICNC57223.2023.10074041"},{"key":"2","unstructured":"[2] C. Takasaki, T. Korikawa, K. Hattori, H. Ohwada, and M. Shimizu, \u201cIoT device identification based on two-stage traffic analysis (encouragement talk),\u201d IEICE Technical Report, NS2022-8, May 2022."},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] Ericsson, \u201cEricsson mobility report,\u201d Nov. 2022.","DOI":"10.23919\/ETR.2022.9954447"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] P. Popovski, K.F. Trillingsgaard, O. Simeone, and G. Durisi, \u201c5G wireless network slicing for eMBB, URLLC, and mMTC: A communication-theoretic view,\u201d IEEE Access, vol.6, pp.55765-55779, 2018. 10.1109\/access.2018.2872781","DOI":"10.1109\/ACCESS.2018.2872781"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] E. Baseman, S. Blanchard, Z. Li, and S. Fu, \u201cRelational synthesis of text and numeric data for anomaly detection on computing system logs,\u201d 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp.882-885, 2016. 10.1109\/icmla.2016.0158","DOI":"10.1109\/ICMLA.2016.0158"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] H. Guo and J. Heidemann, \u201cIP-based IoT device detection,\u201d IoT S&amp;P &apos;18, New York, NY, USA, pp.36-42, Association for Computing Machinery, 2018. 10.1145\/3229565.3229572","DOI":"10.1145\/3229565.3229572"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] J. Bao, B. Hamdaoui, and W.K. Wong, \u201cIoT device type identification using hybrid deep learning approach for increased IoT security,\u201d 2020 International Wireless Communications and Mobile Computing (IWCMC), pp.565-570, 2020. 10.1109\/iwcmc48107.2020.9148110","DOI":"10.1109\/IWCMC48107.2020.9148110"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] H. Noguchi, M. Kataoka, and Y. Yamato, \u201cDevice identification based on communication analysis for the internet of things,\u201d IEEE Access, vol.7, pp.52903-52912, 2019. 10.1109\/access.2019.2910848","DOI":"10.1109\/ACCESS.2019.2910848"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] A. Sivanathan, D. Sherratt, H.H. Gharakheili, A. Radford, C. Wijenayake, A. Vishwanath, and V. Sivaraman, \u201cCharacterizing and classifying iot traffic in smart cities and campuses,\u201d 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp.559-564, 2017. 10.1109\/infcomw.2017.8116438","DOI":"10.1109\/INFCOMW.2017.8116438"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] L. Bai, L. Yao, S.S. Kanhere, X. Wang, and Z. Yang, \u201cAutomatic device classification from network traffic streams of internet of things,\u201d 2018 IEEE 43rd Conference on Local Computer Networks (LCN), pp.1-9, 2018. 10.1109\/lcn.2018.8638232","DOI":"10.1109\/LCN.2018.8638232"},{"key":"11","unstructured":"[11] \u201cThe 3rd generation partnership project,\u201d https:\/\/www.3gpp.org\/"},{"key":"12","unstructured":"[12] \u201cO-ran alliance,\u201d https:\/\/www.o-ran.org\/"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] S. Rezaei and X. Liu, \u201cDeep learning for encrypted traffic classification: An overview,\u201d IEEE Commun. Mag., vol.57, no.5, pp.76-81, 2019. 10.1109\/mcom.2019.1800819","DOI":"10.1109\/MCOM.2019.1800819"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] S. Rezaei and X. Liu, \u201cMultitask learning for network traffic classification,\u201d 2020 29th International Conference on Computer Communications and Networks (ICCCN), IEEE, Aug. 2020. 10.1109\/icccn49398.2020.9209652","DOI":"10.1109\/ICCCN49398.2020.9209652"},{"key":"15","unstructured":"[15] N. Bayat, W. Jackson, and D. Liu, \u201cDeep learning for network traffic classification,\u201d arXiv, arXiv:2106.12693, 2021. 10.48550\/arXiv.2106.12693"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] K. Hatanaka, T. Kimura, Y. Komai, K. Ishibashi, M. Kobayashi, and S. Harada, \u201cExtraction and prediction of user communication behaviors from dns query logs based on nonnegative tensor factorization,\u201d IEEE Trans. Netw. Service Manag., vol.20, no.3, pp.2611-2624, 2023. 10.1109\/tnsm.2023.3238858","DOI":"10.1109\/TNSM.2023.3238858"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] Y. Zhou and J. Cui, \u201cResearch and improvement of encrypted traffic classification based on convolutional neural network,\u201d 2020 IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT), pp.150-154, 2020. 10.1109\/iccsnt50940.2020.9305018","DOI":"10.1109\/ICCSNT50940.2020.9305018"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] M. Lotfollahi, R.S.H. Zade, M.J. Siavoshani, and M. Saberian, \u201cDeep packet: A novel approach for encrypted traffic classification using deep learning,\u201d arXiv, arXiv:1709.02656, 2018. 10.48550\/arXiv.1709.02656","DOI":"10.1007\/s00500-019-04030-2"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] X. Lin, G. Xiong, G. Gou, Z. Li, J. Shi, and J. Yu, \u201cET-BERT: A contextualized datagram representation with pre-training transformers for encrypted traffic classification,\u201d Proc. ACM Web Conference 2022, ACM, April 2022. 10.1145\/3485447.3512217","DOI":"10.1145\/3485447.3512217"},{"key":"20","unstructured":"[20] A. Krizhevsky, I. Sutskever, and G.E. Hinton, \u201cImagenet classification with deep convolutional neural networks,\u201d Proc. 25th International Conference on Neural Information Processing Systems-Volume 1, NIPS&apos;12, Red Hook, NY, USA, p.1097-1105, 2012."},{"key":"21","unstructured":"[21] K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep residual learning for image recognition,\u201d arXiv, arXiv:1512.03385, 2015. 10.48550\/arXiv.1512.03385"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] S. Sinche, D. Raposo, N. Armando, A. Rodrigues, F. Boavida, V. Pereira, and J.S. Silva, \u201cA survey of IoT management protocols and frameworks,\u201d IEEE Commun. Surveys Tuts., vol.22, no.2, pp.1168-1190, 2020. 10.1109\/comst.2019.2943087","DOI":"10.1109\/COMST.2019.2943087"},{"key":"23","unstructured":"[23] \u201cOpenwrt,\u201d https:\/\/openwrt.org\/"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] A. Sivanathan, H.H. Gharakheili, F. Loi, A. Radford, C. Wijenayake, A. Vishwanath, and V. Sivaraman, \u201cClassifying IoT devices in smart environments using network traffic characteristics,\u201d IEEE Trans. Mobile Comput., vol.18, no.8, pp.1745-1759, 2019. 10.1109\/tmc.2018.2866249","DOI":"10.1109\/TMC.2018.2866249"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama, \u201cOptuna: A next-generation hyperparameter optimization framework,\u201d Proc. 25rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.2623-2631, 2019. 10.1145\/3292500.3330701","DOI":"10.1145\/3292500.3330701"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] J. Zhang, F. Li, F. Ye, and H. Wu, \u201cAutonomous unknown-application filtering and labeling for DL-based traffic classifier update,\u201d IEEE INFOCOM 2020-IEEE Conference on Computer Communications, pp.397-405, 2020. 10.1109\/infocom41043.2020.9155292","DOI":"10.1109\/INFOCOM41043.2020.9155292"}],"container-title":["IEICE Transactions on Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transcom\/E107.B\/1\/E107.B_2023WWP0004\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T15:02:34Z","timestamp":1704898954000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transcom\/E107.B\/1\/E107.B_2023WWP0004\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,1]]},"references-count":26,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.1587\/transcom.2023wwp0004","relation":{},"ISSN":["0916-8516","1745-1345"],"issn-type":[{"value":"0916-8516","type":"print"},{"value":"1745-1345","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,1]]},"article-number":"2023WWP0004"}}