{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:30:08Z","timestamp":1776130208266,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819756087","type":"print"},{"value":"9789819756094","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-5609-4_38","type":"book-chapter","created":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T12:02:04Z","timestamp":1722340924000},"page":"482-493","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["An Efficient CNN\u2009+\u2009Sparse Transformer-Based Intrusion Detection Method for IoT"],"prefix":"10.1007","author":[{"given":"Yiying","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifan","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenkun","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianqian","family":"Guan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,31]]},"reference":[{"key":"38_CR1","doi-asserted-by":"publisher","first-page":"32134","DOI":"10.1109\/ACCESS.2023.3259548","volume":"11","author":"L Tao","year":"2023","unstructured":"Tao, L., Xueqiang, M.: Hybrid strategy improved sparrow search algorithm in the field of intrusion detection. IEEE Access 11, 32134\u201332151 (2023)","journal-title":"IEEE Access"},{"issue":"2","key":"38_CR2","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.1109\/JSYST.2020.2992966","volume":"15","author":"D Gumusbas","year":"2020","unstructured":"Gumusbas, D., Y\u0131ld\u0131r\u0131m, T., Genovese, A., et al.: A comprehensive survey of databases and deep learning methods for cybersecurity and intrusion detection systems. IEEE Syst. J. 15(2), 1717\u20131731 (2020)","journal-title":"IEEE Syst. J."},{"issue":"4","key":"38_CR3","doi-asserted-by":"publisher","first-page":"949","DOI":"10.3390\/electronics12040949","volume":"12","author":"J Luo","year":"2023","unstructured":"Luo, J., Zhang, Y., Wu, Y., et al.: A multi-channel contrastive learning network based intrusion detection method. Electronics 12(4), 949 (2023)","journal-title":"Electronics"},{"key":"38_CR4","unstructured":"Child, R., Gray, S., Radford, A., Sutskever, I.: Generating Long Sequences with Sparse Transformers (2019). https:\/\/openai.com\/blog\/sparse-transformers"},{"key":"38_CR5","doi-asserted-by":"crossref","unstructured":"Cevallos, M.J.F., Rizzardi, A., Sicari, S., Porisini, A.C., et al.: Deep reinforcement learning for intrusion detection in internet of things: best practices, lessons learnt, and open challenges. Comput. Netw. 236, 110016 (2023)","DOI":"10.1016\/j.comnet.2023.110016"},{"key":"38_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110941","volume":"279","author":"R Lazzarini","year":"2023","unstructured":"Lazzarini, R., Tianfield, H., Charissis, V., et al.: A stacking ensemble of deep learning models for IoT intrusion detection. Knowl.-Based Syst. 279, 110941 (2023)","journal-title":"Knowl.-Based Syst."},{"key":"38_CR7","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.aej.2023.11.015","volume":"84","author":"F Antonius","year":"2023","unstructured":"Antonius, F., Sekhar, J.C., Rao, V.S., Pradhan, R., Narendran, S., et al.: Unleashing the poer of Bat optimized CNN-BiLSTM model for advanced network anomaly detection: Enhancing security and performance in IoT environments. Alex. Eng. J. 84, 333\u2013342 (2023)","journal-title":"Alex. Eng. J."},{"key":"38_CR8","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.neunet.2022.12.011","volume":"159","author":"SY Diaba","year":"2023","unstructured":"Diaba, S.Y., Elmusrati, M.: Proposed algorithm for smart grid DDoS detection based on deep learning. Neural Netw. 159, 175\u2013184 (2023)","journal-title":"Neural Netw."},{"key":"38_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122564","volume":"241","author":"LD Manocchio","year":"2024","unstructured":"Manocchio, L.D., Layeghy, S., Lo, W.W., Kulatilleke, G.K., Sarhan, M., Portmann, M.: FlowTransformer: A transformer framework for flow-based network intrusion detection systems. Expert Syst. Appl. 241, 122564 (2024)","journal-title":"Expert Syst. Appl."},{"issue":"5","key":"38_CR10","doi-asserted-by":"publisher","first-page":"2754","DOI":"10.3390\/app13052754","volume":"13","author":"T Kim","year":"2023","unstructured":"Kim, T., Pak, W.: Deep learning-based network intrusion detection using multiple image transformers. Appl. Sci. 13(5), 2754 (2023)","journal-title":"Appl. Sci."},{"issue":"10","key":"38_CR11","doi-asserted-by":"publisher","first-page":"6251","DOI":"10.3390\/app13106251","volume":"13","author":"Y Liu","year":"2023","unstructured":"Liu, Y., Wu, L.: Intrusion detection model based on improved transformer. Appl. Sci. 13(10), 6251 (2023)","journal-title":"Appl. Sci."},{"key":"38_CR12","unstructured":"Katharopoulos, A., Vyas, A., Pappas, N., et al. Transformers are RNNs: fast autoregressive transformers with linear attention. In: International Conference on Machine Learning, pp. 5156\u20135165 (2020)"},{"issue":"5","key":"38_CR13","doi-asserted-by":"publisher","first-page":"1711","DOI":"10.1007\/s13042-023-01992-6","volume":"15","author":"H Bao","year":"2024","unstructured":"Bao, H., Dong, L., Wang, W., et al.: Fine-tuning pretrained transformer encoders for sequence-to-sequence learning. Int. J. Mach. Learn. Cybern. 15(5), 1711\u20131728 (2024)","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"38_CR14","doi-asserted-by":"crossref","unstructured":"Tan, J., Lu, X., Zhang, G., et al.: Equalization loss v2: a new gradient balance approach for long-tailed object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1685\u20131694 (2021)","DOI":"10.1109\/CVPR46437.2021.00173"},{"key":"38_CR15","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.comcom.2023.04.018","volume":"205","author":"K Ren","year":"2023","unstructured":"Ren, K., Yuan, S., Zhang, C., Shi, Y., Huang, Z.: CANET: A hierarchical CNN-attention model for network intrusion detection. Comput. Commun. 205, 170\u2013181 (2023)","journal-title":"Comput. Commun."},{"issue":"4","key":"38_CR16","doi-asserted-by":"publisher","first-page":"165","DOI":"10.33851\/JMIS.2019.6.4.165","volume":"6","author":"J Kim","year":"2019","unstructured":"Kim, J., Shin, Y., Choi, E.: An intrusion detection model based on a convolutional neural network. J Multimedia Inform. Syst. 6(4), 165\u2013172 (2019)","journal-title":"J Multimedia Inform. Syst."},{"key":"38_CR17","unstructured":"Powers, D. M. W.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J. sSpecified (2020)"},{"key":"38_CR18","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. Inter. J. 38, 101322 (2023)","journal-title":"Eng. Sci. Technol. Inter. J."},{"key":"38_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.teler.2023.100053","volume":"10","author":"V Hnamte","year":"2023","unstructured":"Hnamte, V., Hussain, J.: DCNNBiLSTM: an efficient hybrid deep learning-based intrusion detection system. Telematics Inform. Rep. 10, 100053 (2023)","journal-title":"Telematics Inform. Rep."},{"key":"38_CR20","doi-asserted-by":"publisher","first-page":"37131","DOI":"10.1109\/ACCESS.2023.3266979","volume":"11","author":"V Hnamte","year":"2023","unstructured":"Hnamte, V., Nhung-Nguyen, H., Hussain, J., Hwa-Kim, Y.: A novel two-stage deep learning model for network intrusion detection: lstm-ae. IEEE Access 11, 37131\u201337148 (2023)","journal-title":"IEEE Access"}],"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\/978-981-97-5609-4_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T12:14:02Z","timestamp":1722341642000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5609-4_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819756087","9789819756094"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5609-4_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"31 July 2024","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":"Tianjin","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2024\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}