{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T16:16:22Z","timestamp":1781194582931,"version":"3.54.1"},"reference-count":34,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2023,9,1]]},"DOI":"10.1587\/transinf.2022ofp0004","type":"journal-article","created":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T23:08:44Z","timestamp":1693523324000},"page":"1480-1489","source":"Crossref","is-referenced-by-count":15,"title":["Few-Shot Learning-Based Malicious IoT Traffic Detection with Prototypical Graph Neural Networks"],"prefix":"10.1587","volume":"E106.D","author":[{"given":"Thin Tharaphe","family":"THEIN","sequence":"first","affiliation":[{"name":"Kobe University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshiaki","family":"SHIRAISHI","sequence":"additional","affiliation":[{"name":"Kobe University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masakatu","family":"MORII","sequence":"additional","affiliation":[{"name":"Kobe University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] Y. Yang, L. Wu, G. Yin, L. Li, and H. Zhao, \u201cA survey on security and privacy issues in Internet-of-Things,\u201d IEEE Internet Things J., vol.4, no.5, pp.1250-1258, April 2017. DOI: 10.1109\/JIOT.2017. 2694844 10.1109\/jiot.2017.2694844","DOI":"10.1109\/JIOT.2017.2694844"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, and W. Zhao, \u201cA survey on internet of things: Architecture, enabling technologies, security and privacy, and applications,\u201d IEEE Internet Things J., vol.4, no.5, pp.1125-1142, March 2017. DOI: 10.1109\/JIOT.2017.2683200 10.1109\/jiot.2017.2683200","DOI":"10.1109\/JIOT.2017.2683200"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] M.B.M. Noor and W.H. Hassan, \u201cCurrent research on Internet of Things (IoT) security: A survey,\u201d Computer networks, vol.148, pp.283-294, Jan. 2019. DOI: 10.1016\/j.comnet.2018.11.025 10.1016\/j.comnet.2018.11.025","DOI":"10.1016\/j.comnet.2018.11.025"},{"key":"4","unstructured":"[4] M. Antonakakis, et al., \u201cUnderstanding the mirai botnet,\u201d in 26th USENIX security symposium (USENIX Security 17), pp.1093-1110, Aug. 2017."},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] G. Thamilarasu and S. Chawla, \u201cTowards deep-learning-driven intrusion detection for the internet of things,\u201d Sensors, vol.19, no.9, p.1977, Jan. 2019. DOI: 10.3390\/s19091977 10.3390\/s19091977","DOI":"10.3390\/s19091977"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] I. Alrashdi, A. Alqazzaz, E. Aloufi, R. Alharthi, M. Zohdy, and H. Ming, \u201cAd-iot: Anomaly detection of iot cyberattacks in smart city using machine learning,\u201d in 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), pp.0305-0310, Jan. 2019. DOI: 10.1109\/CCWC.2019.8666450 10.1109\/ccwc.2019.8666450","DOI":"10.1109\/CCWC.2019.8666450"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] M. Almiani, A. AbuGhazleh, A. Al-Rahayfeh, S. Atiewi, and A. Razaque, \u201cDeep recurrent neural network for IoT intrusion detection system,\u201d Simulation modelling practice and theory, vol.101, p.102031, May 2020. DOI: 10.1016\/j.simpat.2019.102031 10.1016\/j.simpat.2019.102031","DOI":"10.1016\/j.simpat.2019.102031"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] J.L. Leevy, T.M. Khoshgoftaar, and J.M. Peterson, \u201cMitigating class imbalance for iot network intrusion detection: A survey,\u201d in 2021 IEEE Seventh International Conference on Big Data Computing Service and Applications (BigDataService), pp.143-148, Aug. 2021. DOI: 10.1109\/BigDataService52369.2021.00023 10.1109\/bigdataservice52369.2021.00023","DOI":"10.1109\/BigDataService52369.2021.00023"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] L. Bilge and T. Dumitra\u015f, \u201cBefore we knew it: An empirical study of zero-day attacks in the real world,\u201d in Proceedings of the 2012 ACM conference on Computer and communications security, pp.833-844, Oct. 2012. DOI: 10.1145\/2382196.2382284 10.1145\/2382196.2382284","DOI":"10.1145\/2382196.2382284"},{"key":"10","unstructured":"[10] O. Vinyals, C. Blundell, T. Lillicrap, and D. Wierstra, \u201cMatching networks for one shot learning,\u201d Advances in neural information processing systems, vol.29, pp.3630-3638, 2016"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] F. Sung, Y. Yang, L. Zhang, T. Xiang, P.H.S. Torr, and T.M. Hospedales, \u201cLearning to compare: Relation network for few-shot learning,\u201d in Proceedings of the IEEE conference on computer vision and pattern recognition, pp.1199-1208, 2018. DOI: 10.1109\/CVPR.2018.00131 10.1109\/cvpr.2018.00131","DOI":"10.1109\/CVPR.2018.00131"},{"key":"12","unstructured":"[12] J. Snell, K. Swersky, and R. Zemel, \u201cPrototypical networks for few-shot learning,\u201d Advances in neural information processing systems, vol.30, pp.4077-4087, 2017."},{"key":"13","unstructured":"[13] V. Garcia and J. Bruna, \u201cFew-shot learning with graph neural networks,\u201d arXiv preprint arXiv:1711. 04043, 2017."},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] R. Shire, S. Shiaeles, K. Bendiab, B. Ghita, and N. Kolokotronis, \u201cMalware squid: A novel IoT malware traffic analysis framework using convolutional neural network and binary visualisation,\u201d in NEW2AN, 2019. DOI: 10.1007\/978-3-030-30859-9_6 10.1007\/978-3-030-30859-9_6","DOI":"10.1007\/978-3-030-30859-9_6"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] G. Conti, E. Dean, M. Sinda, and B. Sangster, \u201cVisual reverse engineering of binary and data files,\u201d Visualization for computer security, pp.1-17, Sept. 2008. DOI: 10.1007\/978-3-540-85933-8_1 10.1007\/978-3-540-85933-8_1","DOI":"10.1007\/978-3-540-85933-8_1"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] W. Liu, X. Liu, X. Di, and H. Qi, \u201cA novel network intrusion detection algorithm based on Fast Fourier transformation,\u201d in 2019 1st International Conference on Industrial Artificial Intelligence (IAI), pp.1-6, July 2019. DOI: 10.1109\/ICIAI.2019.8850770 10.1109\/iciai.2019.8850770","DOI":"10.1109\/ICIAI.2019.8850770"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] F. Hussain, S.G. Abbas, M. Husnain, U.U. Fayyaz, F. Shahzad, and G.A. Shah, \u201cIOT dos and ddos attack detection using ResNet,\u201d 2020 IEEE 23rd International Multitopic Conference (INMIC), pp.1-6, Nov. 2020. DOI:10.1109\/inmic50486.2020.9318216 10.1109\/inmic50486.2020.9318216","DOI":"10.1109\/INMIC50486.2020.9318216"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] M. Shafiq, Z. Tian, A.K. Bashir, X. Du, and M. Guizani, \u201cCorrAUC: A malicious bot-IoT traffic detection method in IoT network using machine-learning techniques,\u201d IEEE Internet Things J., vol.8, no.5, pp.3242-3254, June 2020. DOI: 10.1109\/JIOT.2020.3002255 10.1109\/jiot.2020.3002255","DOI":"10.1109\/JIOT.2020.3002255"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] G. Bendiab, S. Shiaeles, A. Alruban, and N. Kolokotronis, \u201cIoT malware network traffic classification using visual representation and deep learning,\u201d in 2020 6th IEEE Conference on Network Softwarization (NetSoft), pp.444-449, June 2020. DOI: 10.1109\/ NetSoft48620.2020.9165381 10.1109\/netsoft48620.2020.9165381","DOI":"10.1109\/NetSoft48620.2020.9165381"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] F. Hussain, S.G. Abbas, M. Husnain, U.U. Fayyaz, F. Shahzad, and G.A. Shah, \u201cIoT DoS and DDoS attack detection using ResNet,\u201d in 2020 IEEE 23rd International Multitopic Conference (INMIC), pp.1-6, 2020. DOI: 10.1109\/INMIC50486.2020.9318216 10.1109\/inmic50486.2020.9318216","DOI":"10.1109\/INMIC50486.2020.9318216"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] C. Xu, J. Shen, and X. Du, \u201cA method of few-shot network intrusion detection based on meta-learning framework,\u201d IEEE Trans. Inf. Forensics Security, vol.15, pp.3540-3552, May 2020. DOI: 10.1109\/TIFS.2020.2991876 10.1109\/tifs.2020.2991876","DOI":"10.1109\/TIFS.2020.2991876"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] Y. Yu and N. Bian, \u201cAn intrusion detection method using few-shot learning,\u201d IEEE Access, vol.8, pp.49730-49740, March 2020. DOI: 10.1109\/ACCESS.2020.2980136 10.1109\/access.2020.2980136","DOI":"10.1109\/ACCESS.2020.2980136"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] F. Scarselli, M. Gori, A.C. Tsoi, M. Hagenbuchner, and G. Monfardini, \u201cThe graph neural network model,\u201d IEEE Trans. Neural Netw., vol.20, no.1, pp.61-80, Dec. 2009. DOI: 10.1109\/TNN.2008. 2005605 10.1109\/tnn.2008.2005605","DOI":"10.1109\/TNN.2008.2005605"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] J. Kim, T. Kim, S. Kim, and C.D. Yoo, \u201cEdge-labeling graph neural network for few-shot learning,\u201d in Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp.11-20, 2019. DOI: 10.1109\/CVPR.2019.00010 10.1109\/cvpr.2019.00010","DOI":"10.1109\/CVPR.2019.00010"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] T. Wei, J. Hou, and R. Feng, \u201cFuzzy graph neural network for few-shot learning,\u201d in 2020 International joint conference on neural networks (IJCNN), pp.1-8, July 2020. DOI: 10.1109\/IJCNN48605.2020.9207213 10.1109\/ijcnn48605.2020.9207213","DOI":"10.1109\/IJCNN48605.2020.9207213"},{"key":"26","doi-asserted-by":"publisher","unstructured":"[26] A. Dainotti, A. Pescape, and K.C. Claffy, \u201cIssues and future directions in traffic classification,\u201d IEEE Netw., vol.26, no.1, pp.35-40, Jan. 2012. DOI: 10.1109\/MNET.2012.6135854 10.1109\/mnet.2012.6135854","DOI":"10.1109\/MNET.2012.6135854"},{"key":"27","unstructured":"[27] \u201cVisual analysis of binary files,\u201d http:\/\/binvis.io"},{"key":"28","unstructured":"[28] H. Sagan, Space-filling curves, Springer Science & Business Media, 2012."},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep residual learning for image recognition,\u201d in Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770-778, 2016. DOI: 10.1109\/cvpr.2016.90 10.1109\/cvpr.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, \u201cImagenet: A large-scale hierarchical image database,\u201d in 2009 IEEE conference on computer vision and pattern recognition, pp.248-255, June 2019. DOI: 10.1109\/CVPR. 2009.5206848 10.1109\/cvpr.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"31","unstructured":"[31] T.N. Kipf and M. Welling, \u201cSemi-supervised classification with graph convolutional networks,\u201d arXiv preprint arXiv:1609. 02907, 2016."},{"key":"32","unstructured":"[32] S. Garcia, A. Parmisano, and M.J. Erquiaga, \u201cIoT-23: A labeled dataset with malicious and benign IoT network traffic,\u201d Zenodo, 2020, http:\/\/doi.org\/10.5281\/zenodo.4743746 10.5281\/zenodo.4743746"},{"key":"33","unstructured":"[33] \u201cAn open source network monitoring tool,\u201d https:\/\/zeek.org"},{"key":"34","unstructured":"[34] \u201cSplitCap,\u201d https:\/\/www.netresec.com\/?page=SplitCap"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E106.D\/9\/E106.D_2022OFP0004\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,2]],"date-time":"2023-09-02T04:32:14Z","timestamp":1693629134000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E106.D\/9\/E106.D_2022OFP0004\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,1]]},"references-count":34,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2022ofp0004","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,1]]},"article-number":"2022OFP0004"}}