{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T15:43:23Z","timestamp":1742917403704,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"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_7","type":"book-chapter","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T10:03:00Z","timestamp":1605002580000},"page":"73-88","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Two-Phase Cycle Algorithm Based on Multi-objective Genetic Algorithm and Modified BP Neural Network for Effective Cyber Intrusion Detection"],"prefix":"10.1007","author":[{"given":"Yiguang","family":"Gong","sequence":"first","affiliation":[]},{"given":"Yunping","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Chuanyang","family":"Yin","sequence":"additional","affiliation":[]},{"given":"Zhiyong","family":"Fan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,11]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","unstructured":"Heady, R., Luger, G., Maccabe, A., Servilla, M.: The architecture of a network level intrusion detection system. Technical Report CS90-20, Department of Computer Science, University of New Mexico. Other Inf. PBD 15 Aug 1990 (1990)","DOI":"10.2172\/425295"},{"key":"7_CR2","unstructured":"F-Secure: The state of cyber security 2017 (2017)"},{"key":"7_CR3","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.eswa.2017.07.005","volume":"88","author":"I Manzoor","year":"2017","unstructured":"Manzoor, I., Kumar, N.: A feature reduced intrusion detection system using ANN classifier. Expert Syst. Appl. 88, 249\u2013257 (2017)","journal-title":"Expert Syst. Appl."},{"key":"7_CR4","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.cose.2018.04.010","volume":"77","author":"R Vijayanand","year":"2018","unstructured":"Vijayanand, R., Devaraj, D., Kannapiran, B.: Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection. Comput. Secur. 77, 304\u2013314 (2018)","journal-title":"Comput. Secur."},{"key":"7_CR5","doi-asserted-by":"publisher","first-page":"12060","DOI":"10.1109\/ACCESS.2017.2787719","volume":"6","author":"L Li","year":"2017","unstructured":"Li, L., Yu, Y., Bai, S., Hou, Y., Chen, X.: An effective two-step intrusion detection approach based on binary classification and k-NN. IEEE Access 6, 12060\u201312073 (2017)","journal-title":"IEEE Access"},{"key":"7_CR6","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.procs.2016.06.047","volume":"89","author":"N Farnaaz","year":"2016","unstructured":"Farnaaz, N., Jabbar, M.A.: Random forest modeling for network intrusion detection system. Proc. Comput. Sci. 89, 213\u2013217 (2016)","journal-title":"Proc. Comput. Sci."},{"key":"7_CR7","doi-asserted-by":"publisher","first-page":"41525","DOI":"10.1109\/ACCESS.2019.2895334","volume":"7","author":"R Vinayakumar","year":"2019","unstructured":"Vinayakumar, R., Alazab, M., Soman, K.P., Poornachandran, P., AlNemrat, A., Venkatraman, S.: Deep learning approach for intelligent intrusion detection system. IEEE Access 7, 41525\u201341550 (2019)","journal-title":"IEEE Access"},{"key":"7_CR8","unstructured":"Cemerlic, A., Yang, L., Kizza, J.M.: Network intrusion detection based on bayesian networks. In: Twentieth International Conference on Software Engineering & Knowledge Engineering. DBLP (2008)"},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Cataltepe, Z., Ekmekci, U., Cataltepe, T., et al.: Online feature selected semi-supervised decision trees for network intrusion detection. In: NOMS 2016 - 2016 IEEE\/IFIP Network Operations and Management Symposium. IEEE (2016)","DOI":"10.1109\/NOMS.2016.7502965"},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"Hodo, E., Bellekens, X., Hamilton, A., Dubouilh, P.L., Atkinson, R.: Threat analysis of IoT networks using artificial neural network intrusion detection system. In: 3th International Symposium on Networks, Computers and Communications (ISNCC). IEEE (2016)","DOI":"10.1109\/ISNCC.2016.7746067"},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Anitha, A.A., Arockiam, L.: ANNIDS: artificial neural network based intrusion detection system for Internet of Things. Int. J. Innov. Technol. Explor. Eng. (2019)","DOI":"10.35940\/ijitee.K1875.0981119"},{"key":"7_CR12","unstructured":"Sun, Z., Lyu, P.: Network attack detection based on neural network LSTM (2019)"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Shenfield, A., Day, D., Ayesh, A.: Intelligent intrusion detection systems using artificial neural networks. ICT Express 4, 95\u201399 (2018). S2405959518300493","DOI":"10.1016\/j.icte.2018.04.003"},{"key":"7_CR14","doi-asserted-by":"publisher","unstructured":"Amruta, M., Talhar, N.: Effective denial of service attack detection using artificial neural network for wired LAN. In: Proceedings 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), pp. 229\u2013234 (2016). https:\/\/doi.org\/10.1109\/SCOPES.2016.7955826","DOI":"10.1109\/SCOPES.2016.7955826"},{"issue":"1","key":"7_CR15","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.eswa.2007.10.005","volume":"36","author":"M Paliwal","year":"2009","unstructured":"Paliwal, M., Kumar, U.A.: Neural networks and statistical techniques: a review of applications. Expert Syst. Appl. 36(1), 2\u201317 (2009)","journal-title":"Expert Syst. Appl."},{"key":"7_CR16","doi-asserted-by":"publisher","unstructured":"Ahmad, F., Isa, N.A.M., Hussai, Z.: A genetic algorithm-based multi-objective optimization of an artificial neural network classifier for breast cancer diagnosis. Neural Comput. Appl. 23(5), 1427\u20131435(2013). https:\/\/doi.org\/10.1007\/s00521-012-1092-1","DOI":"10.1007\/s00521-012-1092-1"},{"key":"7_CR17","doi-asserted-by":"publisher","unstructured":"Cao, X.Y., Yu, H.L., Zou, Y.Y.: Character recognition based on genetic algorithm and neural network. In: Lu, W., Cai, G., Liu, W., Xing, W. (eds.) Proceedings of the 2012 International Conference on Information Technology and Software Engineering. LNEE, vol. 211. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-34522-7_96","DOI":"10.1007\/978-3-642-34522-7_96"},{"key":"7_CR18","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/978-3-319-08624-8_8","volume-title":"Intelligent Methods for Cyber Warfare","author":"G Kumar","year":"2015","unstructured":"Kumar, G., Kumar, K.: A multi-objective genetic algorithm based approach for effective intrusion detection using neural networks. In: Yager, R.R., Reformat, M.Z., Alajlan, N. (eds.) Intelligent Methods for Cyber Warfare. SCI, vol. 563, pp. 173\u2013200. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-08624-8_8"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Abbass, H.A.: Pareto neuro-evolution: constructing ensemble of neural networks using multi-objective optimization. In: Proceedings of the 2003 Congress on Evolutionary Computation, CEC 2003, 8\u201312 December 2003, vol. 2073, pp. 2074\u20132080 (2003)","DOI":"10.1109\/CEC.2003.1299928"},{"key":"7_CR20","unstructured":"Fonseca, C., Fleming, P.: Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. In: Proceedings of the 5th International Conference on Genetic Algorithm, University of Illinois, 1993, pp. 416\u2013423. Morgan Kaufmann (1993)"},{"issue":"2","key":"7_CR21","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182\u2013197 (2002)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"4","key":"7_CR22","doi-asserted-by":"publisher","first-page":"1321","DOI":"10.1007\/s00500-017-2856-4","volume":"23","author":"S Elhag","year":"2017","unstructured":"Elhag, S., Fern\u00e1ndez, A., Altalhi, A., Alshomrani, S., Herrera, F.: A multi-objective evolutionary fuzzy system to obtain a broad and accurate set of solutions in intrusion detection systems. Soft. Comput. 23(4), 1321\u20131336 (2017). https:\/\/doi.org\/10.1007\/s00500-017-2856-4","journal-title":"Soft. Comput."},{"key":"7_CR23","doi-asserted-by":"publisher","unstructured":"Stehlik, M., Saleh, A., Stetsko, A., Matyas, V.: Multi-objective optimization of intrusion detection systems for wireless sensor networks, pp. 569\u2013576 (2013). https:\/\/doi.org\/10.7551\/978-0-262-31709-2-ch082","DOI":"10.7551\/978-0-262-31709-2-ch082"},{"issue":"4","key":"7_CR24","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1080\/0305215X.2010.491549","volume":"43","author":"S Tiwari","year":"2011","unstructured":"Tiwari, S., Fadel, G., Deb, K.: Amga2: improving the performance of the archive-based microgenetic algorithm for multi-objective optimization. Eng. Optim. 43(4), 377\u2013401 (2011)","journal-title":"Eng. Optim."},{"key":"7_CR25","unstructured":"Fei, Y., Li, N., et al.: Multiobjective optimization method based on Pareto solution and its application. Lift. Transp. Mach. 9, 13\u201315 (2006)"},{"key":"7_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1007\/BFb0056910","volume-title":"Parallel Problem Solving from Nature \u2014 PPSN V","author":"W Khatib","year":"1998","unstructured":"Khatib, W., Fleming, P.J.: The stud GA: a mini revolution? In: Eiben, A.E., B\u00e4ck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 683\u2013691. Springer, Heidelberg (1998). https:\/\/doi.org\/10.1007\/BFb0056910"},{"key":"7_CR27","unstructured":"Zhu, J.: Non-classical mathematical methods for intelligent systems (2001)"},{"key":"7_CR28","unstructured":"KDD: Kdd cup 1999 dataset (1999). http:\/\/kdd.ics.uci.edu\/databases\/kddcup99\/kddcup99.html"},{"key":"7_CR29","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.eswa.2016.09.041","volume":"67","author":"WL Al-Yaseen","year":"2017","unstructured":"Al-Yaseen, W.L., Othman, Z.A., Nazri, M.Z.A.: Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system. Expert Syst. Appl. 67, 296\u2013303 (2017)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"7_CR30","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s41870-019-00323-9","volume":"12","author":"Y Hamid","year":"2019","unstructured":"Hamid, Y., Sugumaran, M.: A t-SNE based non linear dimension reduction for network intrusion detection. Int. J. Inf. Technol. 12(1), 125\u2013134 (2019). https:\/\/doi.org\/10.1007\/s41870-019-00323-9","journal-title":"Int. J. Inf. Technol."},{"issue":"7","key":"7_CR31","doi-asserted-by":"publisher","first-page":"918","DOI":"10.1016\/j.patrec.2008.01.008","volume":"29","author":"C Xiang","year":"2008","unstructured":"Xiang, C., Yong, P.C., Meng, L.S.: Design of multiple-level hybrid classifier for intrusion detection system using bayesian clustering and decision trees. Pattern Recogn. Lett. 29(7), 918\u2013924 (2008)","journal-title":"Pattern Recogn. Lett."},{"issue":"1","key":"7_CR32","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.jnca.2005.06.003","volume":"30","author":"S Peddabachigari","year":"2007","unstructured":"Peddabachigari, S., Abraham, A., Grosan, C., Thomas, J.: Modeling intrusion detection system using hybrid intelligent systems. J. Netw. Comput. Appl. 30(1), 114\u2013132 (2007)","journal-title":"J. Netw. Comput. Appl."},{"key":"7_CR33","unstructured":"Kadam, P.U., Deshmukh, M.: Real-time intrusion detection with genetic, fuzzy, pattern matching algorithm. In: International Conference on Computing for Sustainable Global Development. IEEE (2016)"}],"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_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T05:41:48Z","timestamp":1723873308000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-62223-7_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030622220","9783030622237"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-62223-7_7","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)"}}]}}