{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T20:06:37Z","timestamp":1779912397029,"version":"3.53.1"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819698745","type":"print"},{"value":"9789819698752","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\/978-981-96-9875-2_7","type":"book-chapter","created":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T09:19:47Z","timestamp":1753089587000},"page":"71-81","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Few-Shot Intrusion Detection Method Combining Model-Agnostic Meta-Learning and Siamese Neural Network"],"prefix":"10.1007","author":[{"given":"Kaiyang","family":"Fang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Runyuan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhifeng","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ge","family":"Zhai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,22]]},"reference":[{"issue":"2","key":"7_CR1","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1109\/TNSM.2023.3334028","volume":"21","author":"N Wei","year":"2023","unstructured":"Wei, N., Yin, L., Tan, J., et al.: An autoencoder-based hybrid detection model for intrusion detection with small-sample problem. IEEE Trans. Netw. Serv. Manage. 21(2), 2402\u20132412 (2023)","journal-title":"IEEE Trans. Netw. Serv. Manage."},{"issue":"1","key":"7_CR2","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1109\/TR.2021.3061297","volume":"71","author":"L Alcantara","year":"2021","unstructured":"Alcantara, L., Padilha, G., Abreu, R., et al.: Syrius: Synthesis of rules for intrusion detectors. IEEE Trans. Reliab. 71(1), 370\u2013381 (2021)","journal-title":"IEEE Trans. Reliab."},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Nicolau, M., McDermott, J.: Learning neural representations for network anomaly detection. IEEE Trans. Cybern. 49(8), 3074\u20133087 (2018)","DOI":"10.1109\/TCYB.2018.2838668"},{"issue":"3","key":"7_CR4","doi-asserted-by":"publisher","first-page":"2515","DOI":"10.1109\/TNSM.2022.3220775","volume":"20","author":"MJ Hashemi","year":"2022","unstructured":"Hashemi, M.J., Keller, E., Tizpaz-Niari, S.: Detecting unseen anomalies in network systems by leveraging neural networks. IEEE Trans. Netw. Serv. Manage. 20(3), 2515\u20132528 (2022)","journal-title":"IEEE Trans. Netw. Serv. Manage."},{"issue":"6","key":"7_CR5","doi-asserted-by":"publisher","first-page":"4280","DOI":"10.1109\/JIOT.2021.3103829","volume":"9","author":"G Abdelmoumin","year":"2021","unstructured":"Abdelmoumin, G., Rawat, D.B., Rahman, A.: On the performance of machine learning models for anomaly-based intelligent intrusion detection systems for the internet of things. IEEE Internet Things J. 9(6), 4280\u20134290 (2021)","journal-title":"IEEE Internet Things J."},{"issue":"6","key":"7_CR6","doi-asserted-by":"publisher","first-page":"4286","DOI":"10.1109\/TII.2021.3133300","volume":"18","author":"B Hu","year":"2021","unstructured":"Hu, B., Bi, Y., Zhi, M., et al.: A deep one-class intrusion detection scheme in software-defined industrial networks. IEEE Trans. Industr. Inf. 18(6), 4286\u20134296 (2021)","journal-title":"IEEE Trans. Industr. Inf."},{"issue":"1","key":"7_CR7","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1109\/JIOT.2022.3201888","volume":"10","author":"Y Chen","year":"2022","unstructured":"Chen, Y., Su, S., Yu, D., et al.: Cross-domain industrial intrusion detection deep model trained with imbalanced data. IEEE Internet Things J. 10(1), 584\u2013596 (2022)","journal-title":"IEEE Internet Things J."},{"key":"7_CR8","doi-asserted-by":"publisher","first-page":"103906","DOI":"10.1109\/ACCESS.2021.3094024","volume":"9","author":"I Ullah","year":"2021","unstructured":"Ullah, I., Mahmoud, Q.H.: Design and development of a deep learning-based model for anomaly detection in IoT networks. IEEE Access 9, 103906\u2013103926 (2021)","journal-title":"IEEE Access"},{"key":"7_CR9","doi-asserted-by":"publisher","first-page":"3540","DOI":"10.1109\/TIFS.2020.2991876","volume":"15","author":"C Xu","year":"2020","unstructured":"Xu, C., Shen, J., Du, X.: A method of few-shot network intrusion detection based on meta-learning framework. IEEE Trans. Inf. Forensics Secur. 15, 3540\u20133552 (2020)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"Du, L., Gu, Z., Wang, Y., et al.: A few-shot class-incremental learning method for net-work intrusion detection. IEEE Trans. Netw. Serv. Manage. (2023)","DOI":"10.1109\/TNSM.2023.3332284"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Wu, Y., Lin, G., Liu, L., et al.: MASiNet: network intrusion detection for IoT security based on meta-learning framework. IEEE Internet of Things J. (2024)","DOI":"10.1109\/JIOT.2024.3395629"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Shi, Z., Xing, M., Zhang, J., Wang, L.: Few-shot network intrusion detection based on model-agnostic meta-learning with l2f method. In: 2023 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1\u20136. IEEE, Glasgow, UK (2023)","DOI":"10.1109\/WCNC55385.2023.10118898"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Feng, T., Qi, Q., Wang, J., Chen, Y.: Few-shot class-adaptive anomaly detection with model-agnostic meta-learning. In: 2021 IFIP Networking Conference (IFIP Networking), pp. 1\u20139. IEEE, Espoo, Finland (2021)","DOI":"10.23919\/IFIPNetworking52078.2021.9472814"},{"key":"7_CR14","unstructured":"Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop, vol. 2, no. 1, pp. 1\u201330. IMLS, Lille, France (2015)"},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1\u20136. IEEE, Ottawa, Canada (2009)","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"Moustafa, N., Slay, J.: UNSW-NB15: a comprehensive data set for network intrusion detection systems. In: 2015 Military Communications and Information Systems Conference (MilCIS), pp. 1\u20136. IEEE, Canberra, Australia (2015)","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Sharafaldin, I., Lashkari, A.H., Hakak, S., Ghorbani, A.A.: Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy. In: 2019 International Carnahan Conference on Security Technology (ICCST), pp. 1\u20138. IEEE, Chennai, India (2019)","DOI":"10.1109\/CCST.2019.8888419"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: Proceedings of the 4th International Conference on Information Systems Security and Privacy (ICISSP), pp. 108\u2013116. SCITEPRESS, Funchal, Portugal (2018)","DOI":"10.5220\/0006639801080116"},{"issue":"3","key":"7_CR19","doi-asserted-by":"publisher","first-page":"2330","DOI":"10.1109\/JIOT.2022.3211346","volume":"10","author":"C Park","year":"2022","unstructured":"Park, C., Lee, J., Kim, Y., et al.: An enhanced AI-based network intrusion detection system using generative adversarial networks. IEEE Internet Things J. 10(3), 2330\u20132345 (2022)","journal-title":"IEEE Internet Things J."},{"key":"7_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110030","volume":"258","author":"E Caville","year":"2022","unstructured":"Caville, E., Lo, W.W., Layeghy, S., et al.: Anomal-E: A self-supervised network intrusion detection system based on graph neural networks. Knowl.-Based Syst. 258, 110030 (2022)","journal-title":"Knowl.-Based Syst."},{"key":"7_CR21","doi-asserted-by":"crossref","unstructured":"Lo, W.W., Layeghy, S., Sarhan, M., Portmann, M.: E-graphsage: A graph neural network based intrusion detection system for iot. In: NOMS 2022\u20132022 IEEE\/IFIP Network Operations and Management Symposium, pp. 1\u20139. IEEE, Budapest, Hungary (2022)","DOI":"10.1109\/NOMS54207.2022.9789878"},{"issue":"11","key":"7_CR22","doi-asserted-by":"publisher","first-page":"12816","DOI":"10.1109\/TPAMI.2022.3200865","volume":"45","author":"DW Zhou","year":"2022","unstructured":"Zhou, D.W., Ye, H.J., Ma, L., et al.: Few-shot class-incremental learning by sampling multi-phase tasks. IEEE Trans. Pattern Anal. Mach. Intell. 45(11), 12816\u201312831 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"7_CR23","doi-asserted-by":"crossref","unstructured":"Manocchio, L.D., Layeghy, S., Lo, W.W., et al.: Flowtransformer: a transformer framework for flow-based network intrusion detection systems. Expert Syst. Appl. 241, 122564 (2024)","DOI":"10.1016\/j.eswa.2023.122564"}],"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\/978-981-96-9875-2_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T19:45:06Z","timestamp":1779911106000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-9875-2_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819698745","9789819698752"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-9875-2_7","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":"22 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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"}}]}}