{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T04:43:45Z","timestamp":1771476225725,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2020,8,1]],"date-time":"2020-08-01T00:00:00Z","timestamp":1596240000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,8,1]],"date-time":"2020-08-01T00:00:00Z","timestamp":1596240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2020,12]]},"DOI":"10.1007\/s00500-020-05200-3","type":"journal-article","created":{"date-parts":[[2020,8,1]],"date-time":"2020-08-01T09:13:51Z","timestamp":1596273231000},"page":"17541-17560","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["A GP-based ensemble classification framework for time-changing streams of intrusion detection data"],"prefix":"10.1007","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8139-3445","authenticated-orcid":false,"given":"Gianluigi","family":"Folino","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesco Sergio","family":"Pisani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luigi","family":"Pontieri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,8,1]]},"reference":[{"key":"5200_CR1","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.ins.2017.06.007","volume":"414","author":"AA Aburomman","year":"2017","unstructured":"Aburomman AA, Reaz MBI (2017) A novel weighted support vector machines multiclass classifier based on differential evolution for intrusion detection systems. Inf Sci 414:225\u2013246","journal-title":"Inf Sci"},{"key":"5200_CR2","doi-asserted-by":"crossref","unstructured":"Acosta-Mendoza N, Morales-Reyes A, Escalante HJ, Gago-Alonso A (2014) Learning to assemble classifiers via genetic programming. IJPRAI 28(7)","DOI":"10.1142\/S0218001414600052"},{"key":"5200_CR3","doi-asserted-by":"crossref","unstructured":"Bifet A, Gavalda R (2007) Learning from time-changing data with adaptive windowing. In: SDM, vol 7. SIAM","DOI":"10.1137\/1.9781611972771.42"},{"issue":"2","key":"5200_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2089094.2089106","volume":"3","author":"A Bifet","year":"2012","unstructured":"Bifet A, Frank E, Holmes G, Pfahringer B (2012) Ensembles of restricted hoeffding trees. ACM Trans Intell Syst Technol (TIST) 3(2):1\u201320","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"5200_CR5","series-title":"Lecture notes in computer science","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1007\/978-3-540-76929-3_24","volume-title":"Advances in computer science\u2014ASIAN 2007. Computer and network security","author":"A Borji","year":"2007","unstructured":"Borji A (2007) Combining heterogeneous classifiers for network intrusion detection. In: Cervesato I (ed) Advances in computer science\u2014ASIAN 2007. Computer and network security, vol 4846. Lecture notes in computer science. Springer, Berlin, pp 254\u2013260"},{"issue":"2","key":"5200_CR6","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/COMST.2015.2494502","volume":"18","author":"AL Buczak","year":"2016","unstructured":"Buczak AL, Guven E (2016) A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun Surv Tutor 18(2):1153\u20131176","journal-title":"IEEE Commun Surv Tutor"},{"key":"5200_CR7","unstructured":"CERT Australia (2012) Cyber crime and security survey report. Technical report, 2012"},{"key":"5200_CR8","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1016\/j.neucom.2018.06.021","volume":"313","author":"VS Costa","year":"2018","unstructured":"Costa VS, Farias ADS, Bedregal B, Santiago RHN, de P Canuto AM, Magaly de A (2018) Combining multiple algorithms in classifier ensembles using generalized mixture functions. Neurocomputing 313:402\u2013414","journal-title":"Neurocomputing"},{"key":"5200_CR9","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.inffus.2017.09.010","volume":"41","author":"RMO Cruz","year":"2018","unstructured":"Cruz RMO, Sabourin R, Cavalcanti GDC (2018) Dynamic classifier selection: recent advances and perspectives. Inf Fusion 41:195\u2013216","journal-title":"Inf Fusion"},{"key":"5200_CR10","doi-asserted-by":"crossref","unstructured":"de\u00a0Oliveira DF, Canuto AMP, de\u00a0Souto MCP (2009) Use of multi-objective genetic algorithms to investigate the diversity\/accuracy dilemma in heterogeneous ensembles. In: International joint conference on neural networks. IEEE, pp 2339\u20132346","DOI":"10.1109\/IJCNN.2009.5178758"},{"key":"5200_CR11","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.ins.2013.09.049","volume":"258","author":"C De Stefano","year":"2014","unstructured":"De Stefano C, Folino G, Fontanella F, Scotto di Freca A (2014) Using bayesian networks for selecting classifiers in GP ensembles. Inf Sci 258:200\u2013216","journal-title":"Inf Sci"},{"key":"5200_CR12","first-page":"1","volume":"7","author":"J Demsar","year":"2006","unstructured":"Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"},{"issue":"C","key":"5200_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jnca.2016.03.011","volume":"66","author":"G Folino","year":"2016","unstructured":"Folino G, Sabatino P (2016) Ensemble based collaborative and distributed intrusion detection systems: a survey. J Netw Comput Appl 66(C):1\u201316","journal-title":"J Netw Comput Appl"},{"issue":"1","key":"5200_CR14","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1109\/TEVC.2002.806168","volume":"7","author":"G Folino","year":"2003","unstructured":"Folino G, Pizzuti C, Spezzano G (2003) A scalable cellular implementation of parallel genetic programming. IEEE Trans Evol Comput 7(1):37\u201353","journal-title":"IEEE Trans Evol Comput"},{"issue":"4","key":"5200_CR15","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1109\/TEVC.2007.906658","volume":"12","author":"G Folino","year":"2008","unstructured":"Folino G, Pizzuti C, Spezzano G (2008) Training distributed GP ensemble with a selective algorithm based on clustering and pruning for pattern classification. IEEE Trans Evol Comput 12(4):458\u2013468","journal-title":"IEEE Trans Evol Comput"},{"key":"5200_CR16","doi-asserted-by":"crossref","unstructured":"Folino G, Pisani FS, Sabatino P (2016a) A distributed intrusion detection framework based on evolved specialized ensembles of classifiers. In: Applications of evolutionary computation\u201419th European conference, EvoApplications 2016, Porto, Portugal, 30 March\u20131 April 2016, Proceedings, Part I, pp 315\u2013331","DOI":"10.1007\/978-3-319-31204-0_21"},{"key":"5200_CR17","doi-asserted-by":"crossref","unstructured":"Folino G, Pisani FS, Sabatino P (2016b) An incremental ensemble evolved by using genetic programming to efficiently detect drifts in cyber security datasets. In: Genetic and evolutionary computation conference, GECCO 2016, Denver, CO, USA, 20\u201324 July 2016, Companion material proceedings, pp 1103\u20131110","DOI":"10.1145\/2908961.2931682"},{"key":"5200_CR18","doi-asserted-by":"crossref","unstructured":"Folino G, Pisani FS, Pontieri L (2019) A cybersecurity framework for classifying non stationary data streams exploiting genetic programming and ensemble learning. In: Numerical computations: theory and algorithms\u20143rd international conference, NUMTA 2019, Crotone, Italy, 15\u201321 June 2019, Revised Selected Papers, Part I, volume 11973 of Lecture notes in computer science, pp 269\u2013277","DOI":"10.1007\/978-3-030-39081-5_24"},{"key":"5200_CR19","doi-asserted-by":"crossref","unstructured":"Gama J, Medas P, Castillo G, Rodrigues P (2004) Learning with drift detection. In: SBIA Brazilian symposium on artificial intelligence. Springer, pp 286\u2013295","DOI":"10.1007\/978-3-540-28645-5_29"},{"key":"5200_CR20","doi-asserted-by":"publisher","first-page":"82512","DOI":"10.1109\/ACCESS.2019.2923640","volume":"7","author":"X Gao","year":"2019","unstructured":"Gao X, Shan C, Hu C, Niu Z, Liu Z (2019) An adaptive ensemble machine learning model for intrusion detection. IEEE Access 7:82512\u201382521","journal-title":"IEEE Access"},{"key":"5200_CR21","first-page":"2677","volume":"9","author":"S Garc\u00eda","year":"2009","unstructured":"Garc\u00eda S, Herrera F (2009) An extension on \u201cstatistical comparisons of classifiers over multiple data sets\u201d for all pairwise comparisons. J Mach Learn Res 9:2677\u20132694","journal-title":"J Mach Learn Res"},{"issue":"18","key":"5200_CR22","doi-asserted-by":"publisher","first-page":"8144","DOI":"10.1016\/j.eswa.2014.07.019","volume":"41","author":"PM Gon\u00e7alves Jr","year":"2014","unstructured":"Gon\u00e7alves PM Jr, de Carvalho\u00a0Santos SGT, de Barros RSM, De Lima Vieira DC (2014) A comparative study on concept drift detectors. Expert Syst Appl 41(18):8144\u20138156","journal-title":"Expert Syst Appl"},{"key":"5200_CR23","doi-asserted-by":"crossref","unstructured":"Hulten G, Spencer L, Domingos P (2001) Mining time-changing data streams. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 97\u2013106","DOI":"10.1145\/502512.502529"},{"key":"5200_CR24","volume-title":"Genetic programming: on the programming of computers by means of natural selection","author":"JR Koza","year":"1992","unstructured":"Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge"},{"issue":"1","key":"5200_CR25","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/s11227-019-03035-w","volume":"76","author":"G Kumar","year":"2020","unstructured":"Kumar G (2020) An improved ensemble approach for effective intrusion detection. J Supercomput 76(1):275\u2013291","journal-title":"J Supercomput"},{"key":"5200_CR26","doi-asserted-by":"publisher","DOI":"10.1002\/0471660264","volume-title":"Combining pattern classifiers: methods and algorithms","author":"L Kuncheva","year":"2004","unstructured":"Kuncheva L (2004) Combining pattern classifiers: methods and algorithms. Wiley-Interscience, New York"},{"key":"5200_CR27","doi-asserted-by":"crossref","unstructured":"Nishida K, Yamauchi K (2007) Detecting concept drift using statistical testing. In: Discovery science. Springer, pp 264\u2013269","DOI":"10.1007\/978-3-540-75488-6_27"},{"key":"5200_CR28","first-page":"105","volume":"2001","author":"N Oza","year":"2001","unstructured":"Oza N (2001) Online bagging and boosting. Proc Artif Intell Stat 2001:105\u2013112","journal-title":"Proc Artif Intell Stat"},{"issue":"6","key":"5200_CR29","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1016\/j.comnet.2008.11.011","volume":"53","author":"R Perdisci","year":"2009","unstructured":"Perdisci R, Ariu D, Fogla P, Giacinto G, Lee W (2009) Mcpad: A multiple classifier system for accurate payload-based anomaly detection. Comput Netw 53(6):864\u2013881 (Traffic classification and its applications to modern networks)","journal-title":"Comput Netw"},{"issue":"2","key":"5200_CR30","first-page":"197","volume":"5","author":"RE Schapire","year":"1990","unstructured":"Schapire RE (1990) The strength of weak learnability. Mach Learn 5(2):197\u2013227","journal-title":"Mach Learn"},{"issue":"2","key":"5200_CR31","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1006\/inco.1995.1136","volume":"121","author":"RE Schapire","year":"1995","unstructured":"Schapire RE (1995) Boosting a weak learning by majority. Inf Comput 121(2):256\u2013285","journal-title":"Inf Comput"},{"issue":"3","key":"5200_CR32","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.cose.2011.12.012","volume":"31","author":"A Shiravi","year":"2012","unstructured":"Shiravi A, Shiravi H, Tavallaee M, Ghorbani AA (2012) Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Comput Secur 31(3):357\u2013374","journal-title":"Comput Secur"},{"issue":"1","key":"5200_CR33","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.eswa.2011.06.013","volume":"39","author":"SSS Sindhu","year":"2012","unstructured":"Sindhu SSS, Geetha S, Kannan A (2012) Decision tree based light weight intrusion detection using a wrapper approach. Expert Syst Appl 39(1):129\u2013141","journal-title":"Expert Syst Appl"},{"key":"5200_CR34","unstructured":"Sylvester J, Chawla NV (2005) Evolutionary ensembles: combining learning agents using genetic algorithms. In: AAAI workshop on multiagent learning, pp 46\u201351"},{"issue":"5","key":"5200_CR35","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1109\/TSMCC.2010.2048428","volume":"40","author":"M Tavallaee","year":"2010","unstructured":"Tavallaee M, Stakhanova N, Ghorbani AA (2010) Toward credible evaluation of anomaly-based intrusion-detection methods. IEEE Trans Syst Man Cybern Part C Appl Rev 40(5):516\u2013524","journal-title":"IEEE Trans Syst Man Cybern Part C Appl Rev"},{"issue":"3","key":"5200_CR36","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/s10994-014-5441-4","volume":"98","author":"I \u017dliobait\u0117","year":"2015","unstructured":"\u017dliobait\u0117 I, Bifet A, Read J, Pfahringer B, Holmes G (2015) Evaluation methods and decision theory for classification of streaming data with temporal dependence. Mach Learn 98(3):455\u2013482","journal-title":"Mach Learn"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-020-05200-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-020-05200-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-020-05200-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,31]],"date-time":"2021-07-31T23:58:14Z","timestamp":1627775894000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-020-05200-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,1]]},"references-count":36,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["5200"],"URL":"https:\/\/doi.org\/10.1007\/s00500-020-05200-3","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,1]]},"assertion":[{"value":"1 August 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}