{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T19:10:04Z","timestamp":1751569804990,"version":"3.41.0"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031877711"},{"type":"electronic","value":"9783031877728"}],"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\/978-3-031-87772-8_20","type":"book-chapter","created":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T07:35:13Z","timestamp":1745307313000},"page":"234-243","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["High-Precision Network Intrusion Detection Method Based on\u00a0NIDS-CNNRF"],"prefix":"10.1007","author":[{"given":"Jiaming","family":"Wang","sequence":"first","affiliation":[]},{"given":"Kai","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Cong","sequence":"additional","affiliation":[]},{"given":"MinJing","family":"Li","sequence":"additional","affiliation":[]},{"given":"Lihui","family":"Bai","sequence":"additional","affiliation":[]},{"given":"Xu An","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,23]]},"reference":[{"issue":"1","key":"20_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s44196-021-00047-4","volume":"14","author":"Md Al-Imran","year":"2021","unstructured":"Al-Imran, Md., Ripon, S.H.: Network intrusion detection: an analytical assessment using deep learning and state-of-the-art machine learning models. Int. J. Comput. Intell. Syst. 14(1), 1\u201320 (2021). https:\/\/doi.org\/10.1007\/s44196-021-00047-4","journal-title":"Int. J. Comput. Intell. Syst."},{"issue":"4","key":"20_CR2","doi-asserted-by":"publisher","first-page":"2276","DOI":"10.3390\/app13042276","volume":"13","author":"S Alzughaibi","year":"2023","unstructured":"Alzughaibi, S., El Khediri, S.: A cloud intrusion detection systems based on DNN using backpropagation and PSO on the CSE-CIC-IDS2018 dataset. Appl. Sci. 13(4), 2276 (2023). https:\/\/doi.org\/10.3390\/app13042276","journal-title":"Appl. Sci."},{"key":"20_CR3","doi-asserted-by":"publisher","unstructured":"Bahashwan, A.A., Anbar, M., Manickam, S., Al-Amiedy, T.A., Aladaileh, M.A., Hasbullah, I.H.: A systematic literature review on machine learning and deep learning approaches for detecting DDOS attacks in software-defined networking. Sensors (Basel) 23(9), 4441 (2023). https:\/\/doi.org\/10.3390\/s23094441, https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/37177643","DOI":"10.3390\/s23094441"},{"key":"20_CR4","doi-asserted-by":"publisher","first-page":"75533","DOI":"10.1007\/s11042-024-18558-5","volume":"83","author":"KC Ramu","year":"2024","unstructured":"Ramu, K.C., Rao, T., Rao, E.: Attack classification in network intrusion detection systems based on optimization strategies and deep learning methods. Multimed. Tools App. 83, 75533\u201375555 (2024). https:\/\/doi.org\/10.1007\/s11042-024-18558-5","journal-title":"Multimed. Tools App."},{"key":"20_CR5","doi-asserted-by":"publisher","unstructured":"Fatani, A., et al.: Enhancing intrusion detection systems for IoT and cloud environments using a growth optimizer algorithm and conventional neural networks. Sensors (Basel) 23(9) (2023). https:\/\/doi.org\/10.3390\/s23094430, https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/37177634","DOI":"10.3390\/s23094430"},{"key":"20_CR6","doi-asserted-by":"publisher","first-page":"63995","DOI":"10.1109\/access.2021.3075066","volume":"9","author":"L Le Jeune","year":"2021","unstructured":"Le Jeune, L., Goedeme, T., Mentens, N.: Machine learning for misuse-based network intrusion detection: overview, unified evaluation and feature choice comparison framework. IEEE Access 9, 63995\u201364015 (2021). https:\/\/doi.org\/10.1109\/access.2021.3075066","journal-title":"IEEE Access"},{"key":"20_CR7","doi-asserted-by":"publisher","first-page":"68807","DOI":"10.1109\/access.2022.3186328","volume":"10","author":"P Liao","year":"2022","unstructured":"Liao, P., Yan, J., Sellier, J.M., Zhang, Y.: Divergence-based transferability analysis for self-adaptive smart grid intrusion detection with transfer learning. IEEE Access 10, 68807\u201368818 (2022). https:\/\/doi.org\/10.1109\/access.2022.3186328","journal-title":"IEEE Access"},{"key":"20_CR8","doi-asserted-by":"publisher","unstructured":"Liu, C., Yin, Y., Sun, Y., Ersoy, O.K.: Multi-scale ResNet and BiGRU automatic sleep staging based on attention mechanism. PLoS One 17(6), e0269,500 (2022). https:\/\/doi.org\/10.1371\/journal.pone.0269500, https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/35709101","DOI":"10.1371\/journal.pone.0269500"},{"key":"20_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/4705982","volume":"2020","author":"G Liu","year":"2020","unstructured":"Liu, G., Zhang, J.: CNID: research of network intrusion detection based on convolutional neural network. Discret. Dyn. Nat. Soc. 2020, 1\u201311 (2020). https:\/\/doi.org\/10.1155\/2020\/4705982","journal-title":"Discret. Dyn. Nat. Soc."},{"key":"20_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.measen.2022.100527","volume":"24","author":"R Kalpana","year":"2022","unstructured":"Kalpana, R.: Recurrent nonsymmetric deep auto encoder approach for network intrusion detection system. Measure. Sens. 24, 100527 (2022). https:\/\/doi.org\/10.1016\/j.measen.2022.100527","journal-title":"Measure. Sens."},{"issue":"3","key":"20_CR11","doi-asserted-by":"publisher","first-page":"668","DOI":"10.3390\/electronics12030668","volume":"12","author":"C Pham-Quoc","year":"2023","unstructured":"Pham-Quoc, C., Bao, T., Thinh, T.N.: FPGA\/AI-powered architecture for anomaly network intrusion detection systems. Electronics 12(3), 668 (2023). https:\/\/doi.org\/10.3390\/electronics12030668","journal-title":"Electronics"},{"key":"20_CR12","doi-asserted-by":"publisher","unstructured":"Shyaa, M.A., Zainol, Z., Abdullah, R., Anbar, M., Alzubaidi, L., Santamaria, J.: Enhanced intrusion detection with data stream classification and concept drift guided by the incremental learning genetic programming combiner. Sensors (Basel) 23(7), 3736 (2023). https:\/\/doi.org\/10.3390\/s23073736","DOI":"10.3390\/s23073736"},{"issue":"1","key":"20_CR13","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1186\/s40537-024-00886-w","volume":"11","author":"MA Talukder","year":"2024","unstructured":"Talukder, M.A., et al.: Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction. J. Big Data 11(1), 33 (2024). https:\/\/doi.org\/10.1186\/s40537-024-00886-w","journal-title":"J. Big Data"},{"key":"20_CR14","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1569","volume":"9","author":"Z Wang","year":"2023","unstructured":"Wang, Z., Chen, H., Yang, S., Luo, X., Li, D., Wang, J.: A lightweight intrusion detection method for IoT based on deep learning and dynamic quantization. PeerJ. Comput. Sci. 9, e1569 (2023)","journal-title":"PeerJ. Comput. Sci."},{"key":"20_CR15","doi-asserted-by":"publisher","first-page":"16062","DOI":"10.1109\/access.2021.3051074","volume":"9","author":"Z Wang","year":"2021","unstructured":"Wang, Z., Zeng, Y., Liu, Y., Li, D.: Deep belief network integrating improved kernel-based extreme learning machine for network intrusion detection. IEEE Access 9, 16062\u201316091 (2021). https:\/\/doi.org\/10.1109\/access.2021.3051074","journal-title":"IEEE Access"},{"issue":"11","key":"20_CR16","doi-asserted-by":"publisher","first-page":"6504","DOI":"10.3390\/app13116504","volume":"13","author":"M Zakariah","year":"2023","unstructured":"Zakariah, M., AlQahtani, S.A., Al-Rakhami, M.S.: Machine learning-based adaptive synthetic sampling technique for intrusion detection. Appl. Sci. 13(11), 6504 (2023). https:\/\/doi.org\/10.3390\/app13116504","journal-title":"Appl. Sci."}],"container-title":["Lecture Notes on Data Engineering and Communications Technologies","Advanced Information Networking and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-87772-8_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T18:37:40Z","timestamp":1751567860000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-87772-8_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031877711","9783031877728"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-87772-8_20","relation":{},"ISSN":["2367-4512","2367-4520"],"issn-type":[{"type":"print","value":"2367-4512"},{"type":"electronic","value":"2367-4520"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"23 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AINA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Information Networking and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Barcelona","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"9 April 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"39","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aina0","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/voyager.ce.fit.ac.jp\/conf\/aina\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}