{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T14:10:19Z","timestamp":1744899019168},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030014179"},{"type":"electronic","value":"9783030014186"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","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":[[2018]]},"DOI":"10.1007\/978-3-030-01418-6_66","type":"book-chapter","created":{"date-parts":[[2018,9,26]],"date-time":"2018-09-26T14:57:36Z","timestamp":1537973856000},"page":"669-681","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Dynamic Ensemble Learning Framework for Data Stream Analysis and Real-Time Threat Detection"],"prefix":"10.1007","author":[{"given":"Konstantinos","family":"Demertzis","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lazaros","family":"Iliadis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vardis-Dimitris","family":"Anezakis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,9,27]]},"reference":[{"issue":"3","key":"66_CR1","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1504\/IJSN.2014.065710","volume":"9","author":"A Ahmim","year":"2014","unstructured":"Ahmim, A., Ghoualmi-Zine, N.: A new adaptive intrusion detection system based on the intersection of two different classifiers. Int. J. Secur. Netw. 9(3), 125\u2013132 (2014)","journal-title":"Int. J. Secur. Netw."},{"issue":"2","key":"66_CR2","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1016\/j.ijforecast.2009.10.008","volume":"27","author":"K Aretz","year":"2011","unstructured":"Aretz, K., Bartram, S.M., Pope, P.F.: Asymmetric loss functions and the rationality of expected stock returns. Int. J. Forecast. 27(2), 413\u2013437 (2011)","journal-title":"Int. J. Forecast."},{"key":"66_CR3","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/978-3-319-17876-9_6","volume-title":"New Frontiers in Mining Complex Patterns","author":"D Brzezinski","year":"2015","unstructured":"Brzezinski, D., Stefanowski, J.: Prequential AUC for classifier evaluation and drift detection in evolving data streams. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z.W. (eds.) NFMCP 2014. LNCS (LNAI), vol. 8983, pp. 87\u2013101. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-17876-9_6"},{"key":"66_CR4","doi-asserted-by":"crossref","unstructured":"Chand, N., Mishra, P., Krishna, C.R., Pilli, E.S., Govil, M.C.: A comparative analysis of SVM and its stacking with other classification algorithm for intrusion detection. In: Proceedings - 2016 International Conference on Advances in Computing, Communication and Automation, ICACCA 2016, pp. 1\u20136 (2016)","DOI":"10.1109\/ICACCA.2016.7578859"},{"key":"66_CR5","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1007\/978-3-319-58801-8_10","volume-title":"Innovations in Enterprise Information Systems Management and Engineering","author":"N Dedi\u0107","year":"2017","unstructured":"Dedi\u0107, N., Stanier, C.: Towards differentiating business intelligence, big data, data analytics and knowledge discovery. In: Piazolo, F., Geist, V., Brehm, L., Schmidt, R. (eds.) ERP Future 2016. LNBIP, vol. 285, pp. 114\u2013122. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-58801-8_10"},{"key":"66_CR6","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/978-3-319-11710-2_2","volume-title":"E-Democracy, Security, Privacy and Trust in a Digital World","author":"K Demertzis","year":"2014","unstructured":"Demertzis, K., Iliadis, L.: A hybrid network anomaly and intrusion detection approach based on evolving spiking neural network classification. In: Sideridis, A.B., Kardasiadou, Z., Yialouris, C.P., Zorkadis, V. (eds.) E-Democracy 2013. CCIS, vol. 441, pp. 11\u201323. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-11710-2_2"},{"key":"66_CR7","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1007\/978-3-319-07869-4_30","volume-title":"Advanced Information Systems Engineering Workshops","author":"K Demertzis","year":"2014","unstructured":"Demertzis, K., Iliadis, L.: Evolving computational intelligence system for malware detection. In: Iliadis, L., Papazoglou, M., Pohl, K. (eds.) CAiSE 2014. LNBIP, vol. 178, pp. 322\u2013334. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-07869-4_30"},{"key":"66_CR8","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/978-3-319-17091-6_17","volume-title":"Statistical Learning and Data Sciences","author":"K Demertzis","year":"2015","unstructured":"Demertzis, K., Iliadis, L.: Evolving smart URL filter in a zone-based policy firewall for detecting algorithmically generated malicious domains. In: Gammerman, A., Vovk, V., Papadopoulos, H. (eds.) SLDS 2015. LNCS (LNAI), vol. 9047, pp. 223\u2013233. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-17091-6_17"},{"key":"66_CR9","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/978-3-319-18275-9_7","volume-title":"Computation, Cryptography, and Network Security","author":"K Demertzis","year":"2015","unstructured":"Demertzis, K., Iliadis, L.: A bio-inspired hybrid artificial intelligence framework for cyber security. In: Daras, N.J., Rassias, M.T. (eds.) Computation, Cryptography, and Network Security, pp. 161\u2013193. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-18275-9_7"},{"key":"66_CR10","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/978-3-319-24306-1_23","volume-title":"Computational Collective Intelligence","author":"K Demertzis","year":"2015","unstructured":"Demertzis, K., Iliadis, L.: SAME: an intelligent anti-malware extension for android ART virtual machine. In: N\u00fa\u00f1ez, M., Nguyen, N.T., Camacho, D., Trawi\u0144ski, B. (eds.) ICCCI 2015. LNCS (LNAI), vol. 9330, pp. 235\u2013245. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24306-1_23"},{"key":"66_CR11","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/978-3-319-27478-2_20","volume-title":"Knowledge, Information and Creativity Support Systems","author":"K Demertzis","year":"2016","unstructured":"Demertzis, K., Iliadis, L.: Bio-inspired hybrid intelligent method for detecting android malware. In: Kunifuji, S., Papadopoulos, G.A., Skulimowski, A.M.J., Kacprzyk, J. (eds.) Knowledge, Information and Creativity Support Systems. AISC, vol. 416, pp. 289\u2013304. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-27478-2_20"},{"issue":"3","key":"66_CR12","first-page":"45","volume":"6","author":"K Demertzis","year":"2016","unstructured":"Demertzis, K., Iliadis, L.: Ladon: a cyber-threat bio-inspired intelligence management system. J. Appl. Math. Bioinf. 6(3), 45\u201364 (2016)","journal-title":"J. Appl. Math. Bioinf."},{"key":"66_CR13","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1007\/978-3-319-65172-9_11","volume-title":"Engineering Applications of Neural Networks","author":"K Demertzis","year":"2017","unstructured":"Demertzis, K., Iliadis, L., Spartalis, S.: A spiking one-class anomaly detection framework for cyber-security on industrial control systems. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds.) EANN 2017. CCIS, vol. 744, pp. 122\u2013134. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-65172-9_11"},{"issue":"1","key":"66_CR14","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1080\/17512549.2017.1325401","volume":"12","author":"K Demertzis","year":"2018","unstructured":"Demertzis, K., Iliadis, L., Anezakis, V.-D.: An innovative soft computing system for smart energy grids cybersecurity. Adv. Build. Energy Res. 12(1), 3\u201324 (2018)","journal-title":"Adv. Build. Energy Res."},{"key":"66_CR15","doi-asserted-by":"crossref","unstructured":"Demertzis, K., Iliadis, L., Anezakis, V.D.: A deep spiking machine-hearing system for the case of invasive fish species. In: 2017 IEEE International Conference on Innovations in Intelligent Systems and Applications, pp. 23\u201328. \u0399\u0395\u0395\u0395 (2017)","DOI":"10.1109\/INISTA.2017.8001126"},{"issue":"DEC","key":"66_CR16","doi-asserted-by":"publisher","first-page":"85","DOI":"10.3389\/fenvs.2017.00085","volume":"5","author":"K Demertzis","year":"2017","unstructured":"Demertzis, K., Iliadis, L., Anezakis, V.-D.: Commentary: Aedes albopictus and Aedes japonicus\u2014two invasive mosquito species with different temperature niches in Europe. Front. Environ. Sci. 5(DEC), 85 (2017)","journal-title":"Front. Environ. Sci."},{"key":"66_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/3-540-45014-9_1","volume-title":"Multiple Classifier Systems","author":"Thomas G Dietterich","year":"2000","unstructured":"Dietterich, Thomas G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1\u201315. Springer, Heidelberg (2000). https:\/\/doi.org\/10.1007\/3-540-45014-9_1"},{"key":"66_CR18","doi-asserted-by":"publisher","first-page":"012042","DOI":"10.1088\/1755-1315\/98\/1\/012042","volume":"98","author":"N. M. Farda","year":"2017","unstructured":"Farda, N.M.: Multi-temporal land use mapping of coastal wetlands area using machine learning in Google earth engine. In: 5th Geoinformation Science Symposium 2017, vol. 98, no. 1, pp. 1\u201312 (2017)","journal-title":"IOP Conference Series: Earth and Environmental Science"},{"issue":"9\u201310","key":"66_CR19","doi-asserted-by":"publisher","first-page":"1469","DOI":"10.1007\/s10994-017-5642-8","volume":"106","author":"HM Gomes","year":"2017","unstructured":"Gomes, H.M., et al.: Adaptive random forests for evolving data stream classification. Mach. Learn. 106(9\u201310), 1469\u20131495 (2017). https:\/\/doi.org\/10.1007\/s10994-017-5642-8","journal-title":"Mach. Learn."},{"key":"66_CR20","series-title":"IFIP Advances in Information and Communication Technology","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1007\/978-3-662-45355-1_9","volume-title":"Critical Infrastructure Protection VIII","author":"W Hurst","year":"2014","unstructured":"Hurst, W., Merabti, M., Fergus, P.: A survey of critical infrastructure security. In: Butts, J., Shenoi, S. (eds.) ICCIP 2014. IAICT, vol. 441, pp. 127\u2013138. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-662-45355-1_9"},{"key":"66_CR21","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.inffus.2017.02.004","volume":"37","author":"B Krawczyk","year":"2017","unstructured":"Krawczyk, B., Minku, L.L., Gama, J., Stefanowski, J., Wo\u017aniak, M.: Ensemble learning for data stream analysis: a survey. Inf. Fus. 37, 132\u2013156 (2017)","journal-title":"Inf. Fus."},{"key":"66_CR22","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1016\/j.asoc.2017.12.008","volume":"68","author":"B Krawczyk","year":"2018","unstructured":"Krawczyk, B., Cano, A.: Online ensemble learning with abstaining classifiers for drifting and noisy data streams. Appl. Soft Comput. 68, 677\u2013692 (2018)","journal-title":"Appl. Soft Comput."},{"key":"66_CR23","doi-asserted-by":"publisher","DOI":"10.1002\/0471660264","volume-title":"Combining Pattern Classifiers: Methods and Algorithms","author":"LI Kuncheva","year":"2004","unstructured":"Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms, 1st edn. Wiley, Hoboken (2004). ISBN 0-471-21078-1","edition":"1"},{"key":"66_CR24","series-title":"Stochastic Modeling and Applied Probability","doi-asserted-by":"publisher","DOI":"10.1007\/b97441","volume-title":"Stochastic Approximation and Recursive Algorithms and Applications","author":"HJ Kushner","year":"2003","unstructured":"Kushner, H.J., Yin, G.G.: Stochastic Approximation and Recursive Algorithms and Applications. Stochastic Modeling and Applied Probability, vol. 35, 2nd edn. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/b97441","edition":"2"},{"issue":"5","key":"66_CR25","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1109\/MIC.2017.3481351","volume":"21","author":"J Lin","year":"2017","unstructured":"Lin, J.: The Lambda and the Kappa. IEEE Internet Comput. 21(5), 60\u201366 (2017)","journal-title":"IEEE Internet Comput."},{"issue":"2","key":"66_CR26","first-page":"226","volume":"35","author":"SM Liu","year":"2017","unstructured":"Liu, S.M., Liu, T., Wang, Z.Q., Xiu, Y., Liu, Y.X., Meng, C.: data stream ensemble classification based on classifier confidence. J. Appl. Sci. 35(2), 226\u2013232 (2017)","journal-title":"J. Appl. Sci."},{"key":"66_CR27","doi-asserted-by":"crossref","unstructured":"Losing, V., Hammer, B., Wersing, H.: KNN classifier with self-adjusting memory for heterogeneous concept drift. In: 16th IEEE International Conference on Data Mining, vol. 7837853, pp. 291\u2013300. IEEE (2017)","DOI":"10.1109\/ICDM.2016.0040"},{"issue":"4","key":"66_CR28","first-page":"26251","volume":"8","author":"MS Rani","year":"2016","unstructured":"Rani, M.S., Sumathy, S.: Analysis of KNN, C5.0 and one class SVM for intrusion detection system. Int. J. Pharm. Technol. 8(4), 26251\u201326259 (2016)","journal-title":"Int. J. Pharm. Technol."},{"issue":"1","key":"66_CR29","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10107-010-0420-4","volume":"127","author":"S Shalev-Shwartz","year":"2011","unstructured":"Shalev-Shwartz, S., Singer, Y., Srebro, N., Cotter, A.: Pegasos: primal estimated sub-gradient solver for SVM. Math. Program. 127(1), 3\u201330 (2011)","journal-title":"Math. Program."},{"key":"66_CR30","unstructured":"Vinagre, J., Jorge, A.M., Gama, J.: Evaluation of recommender systems in streaming environments. In: Workshop on Recommender Systems Evaluation: Dimensions and Design, SV, US, pp. 1\u20136 (2014)"},{"key":"66_CR31","doi-asserted-by":"crossref","unstructured":"Wang, C., Fang, L., Dai, Y.: A simulation environment for SCADA security analysis and assessment. In: Conference on Measuring Technology and Mechatronics Automation, vol. 1, pp. 342\u2013347. IEEE (2010)","DOI":"10.1109\/ICMTMA.2010.603"},{"key":"66_CR32","series-title":"Chapman & Hall\/CRC Machine Learning & Pattern Recognition Series","doi-asserted-by":"publisher","DOI":"10.1201\/b12207","volume-title":"Ensemble Methods: Foundations and Algorithms","author":"ZH Zhou","year":"2012","unstructured":"Zhou, Z.H.: Ensemble Methods: Foundations and Algorithms. Chapman & Hall\/CRC Machine Learning & Pattern Recognition Series, 1st edn. CRC Press, T&F, New York (2012)","edition":"1"},{"issue":"3","key":"66_CR33","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/s10994-014-5441-4","volume":"98","author":"I \u017dliobait\u0117","year":"2014","unstructured":"\u017dliobait\u0117, I., Bifet, A., Read, J., Pfahringer, B., Holmes, G.: Evaluation methods and decision theory for classification of streaming data with temporal dependence. Mach. Learn. 98(3), 455\u2013482 (2014)","journal-title":"Mach. Learn."}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2018"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01418-6_66","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,24]],"date-time":"2019-10-24T22:40:39Z","timestamp":1571956839000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-01418-6_66"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030014179","9783030014186"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01418-6_66","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rhodes","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2018\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"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"}},{"value":"139","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"28","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"39% - 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"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"In addition there are 41 full poster papers and 11 short poster papers included in the proceedings","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}