{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:45:06Z","timestamp":1742913906673,"version":"3.40.3"},"publisher-location":"Cham","reference-count":10,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031200984"},{"type":"electronic","value":"9783031200991"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-20099-1_18","type":"book-chapter","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T15:04:11Z","timestamp":1673535851000},"page":"214-227","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Method of Protecting Sensitive Information in Intangible Cultural Heritage Communication Network Based on Machine Learning"],"prefix":"10.1007","author":[{"given":"Xiaoyu","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ye","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,13]]},"reference":[{"issue":"4","key":"18_CR1","doi-asserted-by":"publisher","first-page":"3243","DOI":"10.1109\/JIOT.2020.2966402","volume":"7","author":"L Gleim","year":"2020","unstructured":"Gleim, L., Bergs, T., Brecher, C., et al.: FactDAG: formalizing data interoperability in an internet of production. IEEE Internet Things J. 7(4), 3243\u20133253 (2020)","journal-title":"IEEE Internet Things J."},{"issue":"5","key":"18_CR2","doi-asserted-by":"publisher","first-page":"413","DOI":"10.3103\/S1068798X2005010X","volume":"40","author":"SY Kalyakulin","year":"2020","unstructured":"Kalyakulin, S.Y., Kuz\u2019Min, V.V., Mitin, E.V., et al.: Automated design of information processing in preproduction. Russ. Eng. Res. 40(5), 413\u2013415 (2020)","journal-title":"Russ. Eng. Res."},{"key":"18_CR3","unstructured":"Wang, L., Xu, Y., Kang, Y.: Simulation of node-level data privacy protection mining method in cloud computing. Comput. Simul. 37(10), 433\u2013436+460 (2020)"},{"issue":"2","key":"18_CR4","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/s11740-021-01023-9","volume":"15","author":"B Denkena","year":"2021","unstructured":"Denkena, B., Behrens, B.A., Bergmann, B., et al.: Potential of process information transfer along the process chain of hybrid components for process monitoring of the cutting process. Prod. Eng. Res. Dev. 15(2), 199\u2013209 (2021)","journal-title":"Prod. Eng. Res. Dev."},{"issue":"1","key":"18_CR5","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/TFUZZ.2020.3006520","volume":"29","author":"L Shuai","year":"2021","unstructured":"Shuai, L., Shuai, W., Xinyu, L., et al.: Fuzzy detection aided real-time and robust visual tracking under complex environments. IEEE Trans. Fuzzy Syst. 29(1), 90\u2013102 (2021)","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"18_CR6","doi-asserted-by":"crossref","unstructured":"Liu, S., et al.: Human memory update strategy: a multi-layer template update mechanism for remote visual monitoring. IEEE Trans. Multimedia 23, 2188\u20132198 (2021)","DOI":"10.1109\/TMM.2021.3065580"},{"issue":"6","key":"18_CR7","first-page":"1","volume":"144","author":"J Yang","year":"2021","unstructured":"Yang, J., Palazzolo, A.: Tilt pad bearing distributed pad inlet temperature with machine learning\u2014Part I: static and dynamic characteristics. J. Tribol. 144(6), 1\u201345 (2021)","journal-title":"J. Tribol."},{"issue":"3137","key":"18_CR8","first-page":"1","volume":"21","author":"V Ostasevicius","year":"2021","unstructured":"Ostasevicius, V., Karpavicius, P., Paulauskaite-Taraseviciene, A., et al.: A machine learning approach for wear monitoring of end mill by self-powering wireless sensor nodes. Sensors 21(3137), 1\u201326 (2021)","journal-title":"Sensors"},{"key":"18_CR9","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1016\/j.neucom.2019.12.143","volume":"458","author":"S Liu","year":"2021","unstructured":"Liu, S., Liu, D., Muhammad, K., Ding, W.: Effective template update mechanism in visual tracking with background clutter. Neurocomputing 458, 615\u2013625 (2021)","journal-title":"Neurocomputing"},{"issue":"1","key":"18_CR10","doi-asserted-by":"publisher","first-page":"83","DOI":"10.2478\/pomr-2021-0008","volume":"28","author":"F Okumu","year":"2021","unstructured":"Okumu, F., Ekmekiolu, A., Kara, S.S.: Modelling ships main and auxiliary engine powers with regression-based machine learning algorithms. Pol. Marit. Res. 28(1), 83\u201396 (2021)","journal-title":"Pol. Marit. Res."}],"container-title":["Lecture Notes in Computer Science","Machine Learning for Cyber Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20099-1_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T15:20:36Z","timestamp":1673536836000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20099-1_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031200984","9783031200991"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20099-1_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"13 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ML4CS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning for Cyber Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ml4cs2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/ml4cs2022\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}