{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,12]],"date-time":"2025-07-12T01:03:08Z","timestamp":1752282188302,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030622220"},{"type":"electronic","value":"9783030622237"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-62223-7_46","type":"book-chapter","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T10:03:00Z","timestamp":1605002580000},"page":"530-539","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Novel Method of Network Security Situation Assessment Based on Evidential Network"],"prefix":"10.1007","author":[{"given":"Xiang","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyang","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,11]]},"reference":[{"issue":"1","key":"46_CR1","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1109\/TIFS.2010.2086445","volume":"6","author":"Z Li","year":"2011","unstructured":"Li, Z., Goyal, A., Chen, Y., et al.: Towards situational awareness of large-scale botnet probing events. IEEE Trans. Inf. Foren. Secur. 6(1), 175\u2013188 (2011)","journal-title":"IEEE Trans. Inf. Foren. Secur."},{"key":"46_CR2","first-page":"131","volume":"7","author":"Z Gong","year":"2010","unstructured":"Gong, Z., Zhuo, Y.: Research on network situational awareness. J. Softw. 7, 131\u2013145 (2010)","journal-title":"J. Softw."},{"issue":"4","key":"46_CR3","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1145\/332051.332079","volume":"43","author":"T Bass","year":"2000","unstructured":"Bass, T.: Intrusion detection systems and multisensor data fusion: creating cyberspace situational awareness. Commun. ACM 43(4), 99\u2013105 (2000)","journal-title":"Commun. ACM"},{"issue":"8","key":"46_CR4","first-page":"2164","volume":"35","author":"Z Wen","year":"2015","unstructured":"Wen, Z., Cao, C., Zhou, H.: Network security situation assessment method based on naive Bayesian classifier. Comput. Appl. 35(8), 2164\u20132168 (2015)","journal-title":"Comput. Appl."},{"key":"46_CR5","unstructured":"Ye, L., Tan, Z.: A network security situation assessment method based on deep learning. Intell. Comput. Appl. 9(06), 73\u201375+82 (2019)"},{"key":"46_CR6","series-title":"Studies in Big Data","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/978-981-10-8476-8_6","volume-title":"Big Data in Engineering Applications","author":"R Vinayakumar","year":"2018","unstructured":"Vinayakumar, R., Poornachandran, P., Soman, K.P.: Scalable framework for cyber threat situational awareness based on domain name systems data analysis. In: Roy, S.S., Samui, P., Deo, R., Ntalampiras, S. (eds.) Big Data in Engineering Applications. SBD, vol. 44, pp. 113\u2013142. Springer, Singapore (2018). https:\/\/doi.org\/10.1007\/978-981-10-8476-8_6"},{"issue":"4","key":"46_CR7","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1360\/jos170885","volume":"17","author":"X Chen","year":"2006","unstructured":"Chen, X., Zheng, Q., Guan, X., et al.: Quantitative hierarchy threat evaluation model for network security. J. Softw. 17(4), 885\u2013897 (2006)","journal-title":"J. Softw."},{"issue":"6","key":"46_CR8","first-page":"1432","volume":"47","author":"H Liu","year":"2019","unstructured":"Liu, H., Liu, J., Hui, X.: Network security situation assessment based on cloud model and Markov Chain. Comput. Dig. Eng. 47(6), 1432\u20131436 (2019)","journal-title":"Comput. Dig. Eng."},{"issue":"2","key":"46_CR9","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1214\/aoms\/1177698950","volume":"38","author":"AP Dempster","year":"1967","unstructured":"Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat. 38(2), 325\u2013339 (1967)","journal-title":"Ann. Math. Stat."},{"key":"46_CR10","unstructured":"Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)"},{"key":"46_CR11","doi-asserted-by":"crossref","unstructured":"Yakowitz, J.: An introduction to Bayesian Networks. Technometrics 39(3), 336\u2013337 (1997)","DOI":"10.1080\/00401706.1997.10485130"},{"issue":"3","key":"46_CR12","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/s10559-007-0061-7","volume":"43","author":"AN Terent\u2019Yev","year":"2007","unstructured":"Terent\u2019Yev, A.N., Bidyuk, P.I.: method of probabilistic inference from learning data in Bayesian networks. Cybern. Syst. Anal. 43(3), 391\u2013396 (2007)","journal-title":"Cybern. Syst. Anal."},{"issue":"7","key":"46_CR13","doi-asserted-by":"publisher","first-page":"950","DOI":"10.1016\/j.ress.2007.03.012","volume":"93","author":"C Simon","year":"2008","unstructured":"Simon, C., Weber, P., Evsukoff, A.: Bayesian networks inference algorithm to implement Dempster Shafer theory in reliability analysis. Reliab. Eng. Syst. Saf. 93(7), 950\u2013963 (2008)","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"46_CR14","doi-asserted-by":"crossref","unstructured":"Appriou, A.: Uncertainty theories and multisensor data fusion. In: ISTE (2014)","DOI":"10.1002\/9781118578636"},{"key":"46_CR15","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.anucene.2018.03.028","volume":"117","author":"X Deng","year":"2018","unstructured":"Deng, X., Jiang, W.: Dependence assessment in human reliability analysis using an evidential network approach extended by belief rules and uncertainty measures. Ann. Nucl. Energy 117, 183\u2013193 (2018)","journal-title":"Ann. Nucl. Energy"},{"issue":"8","key":"46_CR16","first-page":"1603","volume":"46","author":"S Cheng","year":"2018","unstructured":"Cheng, S., Niu, Y., Li, J., Tong, K., et al.: A method of network security situation assessment based on evidential reasoning rules. Comput. Dig. Eng. 46(8), 1603\u20131607 (2018)","journal-title":"Comput. Dig. Eng."}],"container-title":["Lecture Notes in Computer Science","Machine Learning for Cyber Security"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-62223-7_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T10:19:46Z","timestamp":1605003586000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-62223-7_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030622220","9783030622237"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-62223-7_46","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"11 November 2020","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ml4cs2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/ml4cs2020\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"360","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"118","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}