{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T09:23:18Z","timestamp":1770283398921,"version":"3.49.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T00:00:00Z","timestamp":1721260800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T00:00:00Z","timestamp":1721260800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Key Research and Development and Promotion Special Project of Henan Province","award":["232102211060"],"award-info":[{"award-number":["232102211060"]}]},{"name":"the Joint Fund Project of Science and Technology Research and Development Plan of Henan Province","award":["232103810042"],"award-info":[{"award-number":["232103810042"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Inf. Secur."],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s10207-024-00889-x","type":"journal-article","created":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T19:02:01Z","timestamp":1721329321000},"page":"3241-3252","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A few-shot learning based method for industrial internet intrusion detection"],"prefix":"10.1007","volume":"23","author":[{"given":"Yahui","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kejing","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruirui","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,18]]},"reference":[{"key":"889_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2019.101677","volume":"89","author":"D Bhamare","year":"2020","unstructured":"Bhamare, D., Zolanvari, M., Erbad, A., Jain, R., Khan, K., Meskin, N.: Cybersecurity for industrial control systems: a survey. Comput. Secur. 89, 101677 (2020)","journal-title":"Comput. Secur."},{"issue":"5","key":"889_CR2","doi-asserted-by":"publisher","first-page":"2985","DOI":"10.1109\/TII.2020.3023507","volume":"17","author":"M Serror","year":"2020","unstructured":"Serror, M., Hack, S., Henze, M., Schuba, M., Wehrle, K.: Challenges and opportunities in securing the industrial internet of things. IEEE Trans. Industr. Inf. 17(5), 2985\u20132996 (2020)","journal-title":"IEEE Trans. Industr. Inf."},{"issue":"6","key":"889_CR3","doi-asserted-by":"publisher","first-page":"2449","DOI":"10.1007\/s11036-021-01891-6","volume":"27","author":"L Kou","year":"2022","unstructured":"Kou, L., Ding, S., Rao, Y., Xu, W., Zhang, J.: A lightweight intrusion detection model for 5g-enabled industrial internet. Mobile Netw. Appl. 27(6), 2449\u20132458 (2022)","journal-title":"Mobile Netw. Appl."},{"issue":"5","key":"889_CR4","doi-asserted-by":"publisher","first-page":"1250","DOI":"10.1109\/JIOT.2017.2694844","volume":"4","author":"Y Yang","year":"2017","unstructured":"Yang, Y., Wu, L., Yin, G., Li, L., Zhao, H.: A survey on security and privacy issues in internet-of-things. IEEE Internet Things J. 4(5), 1250\u20131258 (2017)","journal-title":"IEEE Internet Things J."},{"issue":"18","key":"889_CR5","doi-asserted-by":"publisher","first-page":"3291","DOI":"10.3390\/math10183291","volume":"10","author":"S Malik","year":"2022","unstructured":"Malik, S., Amin, J., Sharif, M., Yasmin, M., Kadry, S., Anjum, S.: Fractured elbow classification using hand-crafted and deep feature fusion and selection based on whale optimization approach. Mathematics 10(18), 3291 (2022)","journal-title":"Mathematics"},{"key":"889_CR6","doi-asserted-by":"crossref","unstructured":"Abu-Khzam, F.N., Abd El-Wahab, M.M., Haidous, M., Yosri, N.: Learning from obstructions: an effective deep learning approach for minimum vertex cover. Ann. Math. Artif. Intell. 1\u201312, (2022)","DOI":"10.1007\/s10472-022-09813-2"},{"issue":"12","key":"889_CR7","doi-asserted-by":"publisher","first-page":"2250219","DOI":"10.1142\/S021812662250219X","volume":"31","author":"U Tariq","year":"2022","unstructured":"Tariq, U., Ahanger, T.A., Ibrahim, A., Bouteraa, Y.S.: The industrial internet of things (iiot): an anomaly identification and countermeasure method. J. Circuits Syst. Comput. 31(12), 2250219 (2022)","journal-title":"J. Circuits Syst. Comput."},{"issue":"1","key":"889_CR8","first-page":"2585656","volume":"2022","author":"MH Sayour","year":"2022","unstructured":"Sayour, M.H., Kozhaya, S.E., Saab, S.S., et al.: Autonomous robotic manipulation: real-time, deep-learning approach for grasping of unknown objects. J. Robot. 2022(1), 2585656 (2022)","journal-title":"J. Robot."},{"issue":"16","key":"889_CR9","doi-asserted-by":"publisher","first-page":"2872","DOI":"10.3390\/math10162872","volume":"10","author":"J Wang","year":"2022","unstructured":"Wang, J., Li, P., Kong, W., An, R.: Unknown security attack detection of industrial control system by deep learning. Mathematics 10(16), 2872 (2022)","journal-title":"Mathematics"},{"key":"889_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2022.102930","volume":"134","author":"IA Khan","year":"2022","unstructured":"Khan, I.A., Keshk, M., Pi, D., Khan, N., Hussain, Y., Soliman, H.: Enhancing iiot networks protection: a robust security model for attack detection in internet industrial control systems. Ad Hoc Netw. 134, 102930 (2022)","journal-title":"Ad Hoc Netw."},{"issue":"4","key":"889_CR11","doi-asserted-by":"publisher","first-page":"4394","DOI":"10.1109\/TIA.2020.2977872","volume":"56","author":"K Krithivasan","year":"2020","unstructured":"Krithivasan, K., Pravinraj, S., VS, S.S., et al.: Detection of cyberattacks in industrial control systems using enhanced principal component analysis and hypergraph-based convolution neural network (EPCA-HG-CNN). IEEE Trans. Ind. Appl. 56(4), 4394\u20134404 (2020)","journal-title":"IEEE Trans. Ind. Appl."},{"issue":"4","key":"889_CR12","first-page":"513","volume":"7","author":"A Abid","year":"2023","unstructured":"Abid, A., Jemili, F., Korbaa, O.: Distributed deep learning approach for intrusion detection system in industrial control systems based on big data technique and transfer learning. J. Inf. Telecommun. 7(4), 513\u2013541 (2023)","journal-title":"J. Inf. Telecommun."},{"issue":"14","key":"889_CR13","doi-asserted-by":"publisher","first-page":"9425","DOI":"10.1007\/s00500-023-08324-4","volume":"27","author":"R Meddeb","year":"2023","unstructured":"Meddeb, R., Jemili, F., Triki, B., Korbaa, O.: A deep learning-based intrusion detection approach for mobile ad-hoc network. Soft. Comput. 27(14), 9425\u20139439 (2023)","journal-title":"Soft. Comput."},{"key":"889_CR14","volume":"38","author":"HC Altunay","year":"2023","unstructured":"Altunay, H.C., Albayrak, Z.: A hybrid cnn+ lstm-based intrusion detection system for industrial iot networks. Eng. Sci. Technol., Int. J. 38, 101322 (2023)","journal-title":"Eng. Sci. Technol., Int. J."},{"issue":"3","key":"889_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3386252","volume":"53","author":"Y Wang","year":"2020","unstructured":"Wang, Y., Yao, Q., Kwok, J.T., Ni, L.M.: Generalizing from a few examples: a survey on few-shot learning. ACM Comput Surv. (csur) 53(3), 1\u201334 (2020)","journal-title":"ACM Comput Surv. (csur)"},{"issue":"6","key":"889_CR16","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1109\/TAI.2022.3160658","volume":"3","author":"S Das","year":"2022","unstructured":"Das, S., Mullick, S.S., Zelinka, I.: On supervised class-imbalanced learning: an updated perspective and some key challenges. IEEE Trans. Artif. Intell. 3(6), 973\u2013993 (2022)","journal-title":"IEEE Trans. Artif. Intell."},{"key":"889_CR17","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."},{"issue":"9","key":"889_CR18","first-page":"5149","volume":"44","author":"T Hospedales","year":"2021","unstructured":"Hospedales, T., Antoniou, A., Micaelli, P., Storkey, A.: Meta-learning in neural networks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 5149\u20135169 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"22","key":"889_CR19","doi-asserted-by":"publisher","first-page":"3839","DOI":"10.3390\/rs12223839","volume":"12","author":"X Tian","year":"2020","unstructured":"Tian, X., Chen, L., Zhang, X., Chen, E.: Improved prototypical network model for forest species classification in complex stand. Remote Sens. 12(22), 3839 (2020)","journal-title":"Remote Sens."},{"issue":"3","key":"889_CR20","doi-asserted-by":"publisher","first-page":"1406","DOI":"10.1109\/TNNLS.2021.3105377","volume":"34","author":"Y Xiao","year":"2021","unstructured":"Xiao, Y., Jin, Y., Hao, K.: Adaptive prototypical networks with label words and joint representation learning for few-shot relation classification. IEEE Trans. Neural Netw. Learn. Syst. 34(3), 1406\u20131417 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"8","key":"889_CR21","doi-asserted-by":"publisher","first-page":"5790","DOI":"10.1109\/TII.2020.3047675","volume":"17","author":"X Zhou","year":"2020","unstructured":"Zhou, X., Liang, W., Shimizu, S., Ma, J., Jin, Q.: Siamese neural network based few-shot learning for anomaly detection in industrial cyber-physical systems. IEEE Trans. Industr. Inf. 17(8), 5790\u20135798 (2020)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"889_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2022.102899","volume":"122","author":"J Yang","year":"2022","unstructured":"Yang, J., Li, H., Shao, S., Zou, F., Wu, Y.: Fs-ids: a framework for intrusion detection based on few-shot learning. Comput. Secur. 122, 102899 (2022)","journal-title":"Comput. Secur."},{"key":"889_CR23","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1016\/j.procs.2020.04.085","volume":"171","author":"P Bedi","year":"2020","unstructured":"Bedi, P., Gupta, N., Jindal, V.: Siam-ids: handling class imbalance problem in intrusion detection systems using siamese neural network. Procedia Comput. Sci. 171, 780\u2013789 (2020)","journal-title":"Procedia Comput. Sci."},{"issue":"8","key":"889_CR24","doi-asserted-by":"publisher","first-page":"5087","DOI":"10.1109\/TII.2021.3116085","volume":"18","author":"W Liang","year":"2021","unstructured":"Liang, W., Hu, Y., Zhou, X., Pan, Y., Kevin, I., Wang, K.: Variational few-shot learning for microservice-oriented intrusion detection in distributed industrial iot. IEEE Trans. Industr. Inf. 18(8), 5087\u20135095 (2021)","journal-title":"IEEE Trans. Industr. Inf."},{"issue":"5","key":"889_CR25","doi-asserted-by":"publisher","first-page":"2351","DOI":"10.3390\/app12052351","volume":"12","author":"AS Iliyasu","year":"2022","unstructured":"Iliyasu, A.S., Abdurrahman, U.A., Zheng, L.: Few-shot network intrusion detection using discriminative representation learning with supervised autoencoder. Appl. Sci. 12(5), 2351 (2022)","journal-title":"Appl. Sci."},{"key":"889_CR26","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. Adv. Neural Inf. Process. Syst.30 (2017)"},{"key":"889_CR27","first-page":"108","volume":"1","author":"I Sharafaldin","year":"2018","unstructured":"Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A., et al.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp 1, 108\u2013116 (2018)","journal-title":"ICISSp"},{"key":"889_CR28","unstructured":"Morris, T., Gao, W.: Industrial control system traffic data sets for intrusion detection research. In: Critical Infrastructure Protection VIII: 8th IFIP WG 11.10 International Conference, ICCIP 2014, Arlington, VA, USA, March 17-19, 2014, Revised Selected Papers 8. 65\u201378. Springer (2014)"},{"key":"889_CR29","unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. Adv. Neural Inf. Process. Syst. 29 (2016)"},{"key":"889_CR30","doi-asserted-by":"crossref","unstructured":"Ingre, B., Yadav, A.: Performance analysis of nsl-kdd dataset using ann. In: 2015 international conference on signal processing and communication engineering systems. 92\u201396. IEEE (2015)","DOI":"10.1109\/SPACES.2015.7058223"}],"container-title":["International Journal of Information Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10207-024-00889-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10207-024-00889-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10207-024-00889-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T01:05:16Z","timestamp":1726275916000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10207-024-00889-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,18]]},"references-count":30,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["889"],"URL":"https:\/\/doi.org\/10.1007\/s10207-024-00889-x","relation":{},"ISSN":["1615-5262","1615-5270"],"issn-type":[{"value":"1615-5262","type":"print"},{"value":"1615-5270","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,18]]},"assertion":[{"value":"18 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"I confirm that the work presented in this research article is original and has not been published elsewhere, nor is it under consideration for publication elsewhere.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Also, we have no Conflict of interest to disclose.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}