{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T18:35:30Z","timestamp":1743014130767,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"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_1","type":"book-chapter","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T10:03:00Z","timestamp":1605002580000},"page":"1-11","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dynamic Adjusting ABC-SVM Anomaly Detection Based on Weighted Function Code Correlation"],"prefix":"10.1007","author":[{"given":"Ming","family":"Wan","sequence":"first","affiliation":[]},{"given":"Jinfang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Luo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3044-9047","authenticated-orcid":false,"given":"Kai","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yingjie","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Bailing","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,11]]},"reference":[{"issue":"2","key":"1_CR1","doi-asserted-by":"publisher","first-page":"860","DOI":"10.1109\/SURV.2012.071812.00124","volume":"15","author":"B Galloway","year":"2013","unstructured":"Galloway, B., Hancke, G.P.: Introduction to industrial control networks. IEEE Commun. Surv. Tutor. 15(2), 860\u2013880 (2013)","journal-title":"IEEE Commun. Surv. Tutor."},{"issue":"1","key":"1_CR2","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1109\/TII.2012.2198666","volume":"9","author":"M Cheminod","year":"2013","unstructured":"Cheminod, M., Durante, L., Valenzano, A.: Review of security issues in industrial networks. IEEE Trans. Industr. Inf. 9(1), 277\u2013293 (2013)","journal-title":"IEEE Trans. Industr. Inf."},{"issue":"1","key":"1_CR3","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1080\/23742917.2016.1252211","volume":"1","author":"UPD Ani","year":"2017","unstructured":"Ani, U.P.D., He, H., Tiwari, A.: Review of cybersecurity issues in industrial critical infrastructure: manufacturing in perspective. J. Cyber Secur. Technol. 1(1), 32\u201374 (2017)","journal-title":"J. Cyber Secur. Technol."},{"key":"1_CR4","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/978-3-662-48634-4_10","volume-title":"Requirements Engineering in the Big Data Era","author":"B-J Kim","year":"2015","unstructured":"Kim, B.-J., Lee, S.-W.: Conceptual framework for understanding security requirements: a preliminary study on stuxnet. In: Liu, L., Aoyama, M. (eds.) Requirements Engineering in the Big Data Era. CCIS, vol. 558, pp. 135\u2013146. Springer, Heidelberg (2015). https:\/\/doi.org\/10.1007\/978-3-662-48634-4_10"},{"issue":"3","key":"1_CR5","doi-asserted-by":"publisher","first-page":"1504","DOI":"10.1109\/COMST.2017.2691349","volume":"19","author":"JQ Li","year":"2017","unstructured":"Li, J.Q., Yu, F.R., Deng, G., Luo, C., Ming, Z., Yan, Q.: Industrial internet: a survey on the enabling technologies, applications, and challenges. IEEE Commun. Surv. Tutor. 19(3), 1504\u20131526 (2017)","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"1_CR6","series-title":"IFIP Advances in Information and Communication Technology","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/978-3-030-34647-8_15","volume-title":"Critical Infrastructure Protection XIII","author":"R Chan","year":"2019","unstructured":"Chan, R., Chow, K.-P., Chan, C.-F.: Defining attack patterns for industrial control systems. In: Staggs, J., Shenoi, S. (eds.) ICCIP 2019. IAICT, vol. 570, pp. 289\u2013309. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-34647-8_15"},{"issue":"4","key":"1_CR7","first-page":"1049","volume":"8","author":"S Han","year":"2014","unstructured":"Han, S., Xie, M., Chen, H.H., Ling, Y.: Intrusion detection in cyber-physical systems: techniques and challenges. IEEE Syst. J. 8(4), 1049\u20131059 (2014)","journal-title":"IEEE Syst. J."},{"issue":"5","key":"1_CR8","doi-asserted-by":"publisher","first-page":"4257","DOI":"10.1109\/TIE.2017.2772190","volume":"65","author":"J Yang","year":"2018","unstructured":"Yang, J., Zhou, C., Yang, S., Xu, H., Hu, B.: Anomaly detection based on zone partition for security protection of industrial cyber-physical systems. IEEE Trans. Industr. Electron. 65(5), 4257\u20134267 (2018)","journal-title":"IEEE Trans. Industr. Electron."},{"issue":"2","key":"1_CR9","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1109\/TDSC.2015.2443793","volume":"13","author":"S Ponomarev","year":"2016","unstructured":"Ponomarev, S., Atkison, T.: Industrial control system network intrusion detection by telemetry analysis. IEEE Trans. Dependable Secure Comput. 13(2), 252\u2013260 (2016)","journal-title":"IEEE Trans. Dependable Secure Comput."},{"issue":"12","key":"1_CR10","doi-asserted-by":"publisher","first-page":"3011","DOI":"10.1109\/TIFS.2017.2730581","volume":"12","author":"M Wan","year":"2017","unstructured":"Wan, M., Shang, W., Zeng, P.: Double behavior characteristics for one-class classification anomaly detection in networked control systems. IEEE Trans. Inf. Forensics Secur. 12(12), 3011\u20133023 (2017)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Fachkha, C.: Cyber threat investigation of SCADA modbus activities. In: 2019 IFIP-NTMS, Canary Islands, Spain, pp. 1\u20137. IEEE (2019)","DOI":"10.1109\/NTMS.2019.8763817"},{"issue":"4","key":"1_CR12","doi-asserted-by":"publisher","first-page":"726","DOI":"10.1109\/TFUZZ.2010.2047947","volume":"18","author":"Z Deng","year":"2010","unstructured":"Deng, Z., Chung, F.L., Wang, S.: Robust relief-feature weighting, margin maximization, and fuzzy optimization. IEEE Trans. Fuzzy Syst. 18(4), 726\u2013744 (2010)","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"4","key":"1_CR13","doi-asserted-by":"publisher","first-page":"2901","DOI":"10.1109\/JIOT.2020.2963927","volume":"7","author":"N Jiang","year":"2020","unstructured":"Jiang, N., Tian, F., Li, J., Yuan, X., Zheng, J.Q.: MAN: mutual attention neural networks model for aspect-level sentiment classification in SIoT. IEEE Internet Things J. 7(4), 2901\u20132913 (2020)","journal-title":"IEEE Internet Things J."},{"issue":"1","key":"1_CR14","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1109\/COMST.2018.2847722","volume":"21","author":"P Mishra","year":"2019","unstructured":"Mishra, P., Varadharajan, V., Tupakula, U., Pilli, E.S.: A detailed investigation and analysis of using machine learning techniques for intrusion detection. IEEE Commun. Surv. Tutor. 21(1), 686\u2013728 (2019)","journal-title":"IEEE Commun. Surv. Tutor."},{"issue":"1","key":"1_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13174-018-0087-2","volume":"9","author":"R Boutaba","year":"2018","unstructured":"Boutaba, R., et al.: A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. J. Internet Serv. Appl. 9(1), 1\u201399 (2018). https:\/\/doi.org\/10.1186\/s13174-018-0087-2","journal-title":"J. Internet Serv. Appl."},{"issue":"3","key":"1_CR16","doi-asserted-by":"publisher","first-page":"1644","DOI":"10.1109\/JSYST.2014.2341597","volume":"11","author":"M Esmalifalak","year":"2017","unstructured":"Esmalifalak, M., Liu, L., Nguyen, N., Zheng, R., Han, Z.: Detecting stealthy false data injection using machine learning in smart grid. IEEE Syst. J. 11(3), 1644\u20131652 (2017)","journal-title":"IEEE Syst. J."},{"issue":"1","key":"1_CR17","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.ins.2011.09.005","volume":"182","author":"M El-Abd","year":"2012","unstructured":"El-Abd, M.: Performance assessment of foraging algorithms vs. evolutionary algorithms. Inf. Sci. 182(1), 243\u2013263 (2012)","journal-title":"Inf. Sci."},{"key":"1_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2019.09.068","volume":"512","author":"N Jiang","year":"2020","unstructured":"Jiang, N., Xu, D., Zhou, J., Yan, H.Y., Wan, T., Zheng, J.Q.: Toward optimal participant decisions with voting-based incentive model for crowd sensing. Inf. Sci. 512, 1\u201317 (2020)","journal-title":"Inf. Sci."},{"issue":"3","key":"1_CR19","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1109\/TCSS.2019.2907059","volume":"6","author":"YJ Wang","year":"2019","unstructured":"Wang, Y.J., Cai, Z.P., Zhan, Z.H., Gong, Y.J., Tong, X.R.: An optimization and auction-based incentive mechanism to maximize social welfare for mobile crowdsourcing. IEEE Trans. Comput. Soc. Syst. 6(3), 414\u2013429 (2019)","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"1_CR20","first-page":"107","volume":"171","author":"YJ Wang","year":"2020","unstructured":"Wang, Y.J., Gao, Y., Li, Y.S., Tong, X.R.: A worker-selection incentive mechanism for optimizing platform-centric mobile crowdsourcing systems. Comput. Netw. 171, 107\u2013144 (2020)","journal-title":"Comput. Netw."},{"key":"1_CR21","doi-asserted-by":"publisher","first-page":"73829","DOI":"10.1109\/ACCESS.2018.2880814","volume":"6","author":"W Gao","year":"2018","unstructured":"Gao, W., Huang, L., Luo, Y., Wei, Z., Liu, S.: Constrained optimization by artificial bee colony framework. IEEE Access 6, 73829\u201373845 (2018)","journal-title":"IEEE Access"},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Bi, J., Zhang, K., Cheng, X.J.: Intrusion detection based on RBF neural network. In: 2009 International Symposium on Information Engineering and Electronic Commerce, Ternopil, Ukraine, pp. 357\u2013360. IEEE (2009)","DOI":"10.1109\/IEEC.2009.80"},{"key":"1_CR23","series-title":"Lecture Notes in Electrical Engineering","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/978-1-4614-3872-4_49","volume-title":"Proceedings of the 2012 International Conference on Cybernetics and Informatics","author":"Z Zhao","year":"2014","unstructured":"Zhao, Z.: Study and application of BP neural network in intrusion detection. In: Zhong, S. (ed.) Proceedings of the 2012 International Conference on Cybernetics and Informatics. LNEE, vol. 163, pp. 379\u2013385. Springer, New York (2014). https:\/\/doi.org\/10.1007\/978-1-4614-3872-4_49"},{"key":"1_CR24","doi-asserted-by":"crossref","unstructured":"Jeldi, S.B.: A review of intrusion detection system using various decision tree algorithm optimize challenges issues. In: 2018 CTEMS, Belgaum, India, pp. 272\u2013275. IEEE (2018)","DOI":"10.1109\/CTEMS.2018.8769228"}],"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_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T10:05:10Z","timestamp":1605002710000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-62223-7_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030622220","9783030622237"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-62223-7_1","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)"}}]}}