{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T13:31:41Z","timestamp":1781098301492,"version":"3.54.1"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2020,5,21]],"date-time":"2020-05-21T00:00:00Z","timestamp":1590019200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,5,21]],"date-time":"2020-05-21T00:00:00Z","timestamp":1590019200000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s00521-020-04986-5","type":"journal-article","created":{"date-parts":[[2020,5,21]],"date-time":"2020-05-21T10:03:07Z","timestamp":1590055387000},"page":"15387-15395","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":118,"title":["A stacked ensemble learning model for intrusion detection in wireless network"],"prefix":"10.1007","volume":"34","author":[{"given":"Hariharan","family":"Rajadurai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Usha Devi","family":"Gandhi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,5,21]]},"reference":[{"key":"4986_CR1","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1109\/TSE.1987.232894","volume":"2","author":"DE Denning","year":"1987","unstructured":"Denning DE (1987) An intrusion-detection model. IEEE Trans Softw Eng 2:222\u2013232","journal-title":"IEEE Trans Softw Eng"},{"issue":"1","key":"4986_CR2","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"key":"4986_CR3","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29:1189\u20131232","journal-title":"Ann Stat"},{"key":"4986_CR4","doi-asserted-by":"crossref","unstructured":"Aung YY, Min MM (2017) An analysis of random forest algorithm based network intrusion detection system. In: 2017 18th IEEE\/ACIS international conference on software engineering, artificial intelligence, networking and parallel\/distributed computing (SNPD). IEEE","DOI":"10.1109\/SNPD.2017.8022711"},{"issue":"1","key":"4986_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LSENS.2018.2879990","volume":"3","author":"R Abdulhammed","year":"2019","unstructured":"Abdulhammed R et al (2019) Deep and machine learning approaches for anomaly-based intrusion detection of imbalanced network traffic. IEEE Sens Lett 3(1):1\u20134","journal-title":"IEEE Sens Lett"},{"key":"4986_CR6","doi-asserted-by":"publisher","first-page":"33789","DOI":"10.1109\/ACCESS.2018.2841987","volume":"6","author":"I Ahmad","year":"2018","unstructured":"Ahmad I et al (2018) Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection. IEEE Access 6:33789\u201333795","journal-title":"IEEE Access"},{"key":"4986_CR7","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.cose.2016.11.004","volume":"65","author":"AA Aburomman","year":"2017","unstructured":"Aburomman AA, Reaz MBI (2017) A survey of intrusion detection systems based on ensemble and hybrid classifiers. Comput Secur 65:135\u2013152","journal-title":"Comput Secur"},{"key":"4986_CR8","doi-asserted-by":"crossref","unstructured":"Choudhury S, Bhowal A (2015) Comparative analysis of machine learning algorithms along with classifiers for network intrusion detection. In: 2015 international conference on smart technologies and management for computing, communication, controls, energy and materials (ICSTM). IEEE","DOI":"10.1109\/ICSTM.2015.7225395"},{"key":"4986_CR9","doi-asserted-by":"crossref","unstructured":"Chang Y, Li W, Yang Z (2017) Network intrusion detection based on random forest and support vector machine. In: 2017 IEEE international conference on computational science and engineering (CSE) and IEEE international conference on embedded and ubiquitous computing (EUC), vol 1. IEEE","DOI":"10.1109\/CSE-EUC.2017.118"},{"key":"4986_CR10","doi-asserted-by":"crossref","unstructured":"Chabathula KJ, Jaidhar CD, Ajay Kumara MA (2015) Comparative study of principal component analysis based intrusion detection approach using machine learning algorithms. In: 2015 3rd international conference on signal processing, communication and networking (ICSCN). IEEE","DOI":"10.1109\/ICSCN.2015.7219853"},{"issue":"11","key":"4986_CR11","first-page":"4349","volume":"2","author":"J Sing","year":"2013","unstructured":"Sing J, Nene MJ (2013) A survey on machine learning techniques for intrusion detection systems. Int J Adv Res Comput Commun Eng 2(11):4349\u20134355","journal-title":"Int J Adv Res Comput Commun Eng"},{"issue":"2","key":"4986_CR12","first-page":"961","volume":"2","author":"M Joshi","year":"2012","unstructured":"Joshi M (2012) Classification, clustering and intrusion detection system. Int J Eng Res Appl (IHERA) 2(2):961\u2013964","journal-title":"Int J Eng Res Appl (IHERA)"},{"issue":"5","key":"4986_CR13","first-page":"202","volume":"2","author":"JA Khan","year":"2016","unstructured":"Khan JA, Jain N (2016) A survey on intrusion detection systems and classification techniques. Int J Sci Res Sci Eng Technol 2(5):202\u2013208","journal-title":"Int J Sci Res Sci Eng Technol"},{"key":"4986_CR14","doi-asserted-by":"crossref","unstructured":"Li H, et al (2018) A RF-PSO based hybrid feature selection model in intrusion detection system. In: 2018 IEEE 3rd international conference on data science in cyberspace (DSC). IEEE","DOI":"10.1109\/DSC.2018.00128"},{"issue":"6","key":"4986_CR15","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1049\/iet-net.2018.5080","volume":"7","author":"M Latah","year":"2018","unstructured":"Latah M, Toker L (2018) Towards an efficient anomaly-based intrusion detection for software-defined networks. IET Netw 7(6):453\u2013459","journal-title":"IET Netw"},{"issue":"16","key":"4986_CR16","doi-asserted-by":"publisher","first-page":"2646","DOI":"10.1002\/sec.508","volume":"8","author":"AJ Malik","year":"2015","unstructured":"Malik AJ, Shahzad W, Khan FA (2015) Network intrusion detection using hybrid binary PSO and random forests algorithm. Secur Commun Netw 8(16):2646\u20132660","journal-title":"Secur Commun Netw"},{"issue":"1","key":"4986_CR17","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1504\/IJEWM.2018.091308","volume":"21","author":"NS Murugan","year":"2018","unstructured":"Murugan NS, Devi GU (2018) Detecting spams in social networks using ML algorithms\u2014a review. Int J Environ Waste Manag 21(1):22\u201336","journal-title":"Int J Environ Waste Manag"},{"key":"4986_CR18","doi-asserted-by":"crossref","unstructured":"Maniriho P, Ahmad T (2018) Analyzing the performance of machine learning algorithms in anomaly network intrusion detection systems. In: 2018 4th international conference on science and technology (ICST), vol 1. IEEE","DOI":"10.1109\/ICSTC.2018.8528645"},{"issue":"10","key":"4986_CR19","doi-asserted-by":"publisher","first-page":"11994","DOI":"10.1016\/j.eswa.2009.05.029","volume":"36","author":"CF Tsai","year":"2009","unstructured":"Tsai CF, Hsu YF, Lin CY, Lin WY (2009) Intrusion detection by machine learning: a review. Expert Syst Appl 36(10):11994\u201312000","journal-title":"Expert Syst Appl"},{"key":"4986_CR20","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.knosys.2018.02.015","volume":"147","author":"C-R Wang","year":"2018","unstructured":"Wang C-R et al (2018) Network intrusion detection using equality constrained-optimization-based extreme learning machines. Knowl Based Syst 147:68\u201380","journal-title":"Knowl Based Syst"},{"issue":"5","key":"4986_CR21","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1109\/TSMCC.2008.923876","volume":"38","author":"J Zhang","year":"2008","unstructured":"Zhang J, Zulkernine M, Haque A (2008) Random-forests-based network intrusion detection systems. IEEE Trans Syst Man Cybern Part C Appl Rev 38(5):649\u2013659","journal-title":"IEEE Trans Syst Man Cybern Part C Appl Rev"},{"key":"4986_CR22","doi-asserted-by":"publisher","first-page":"21954","DOI":"10.1109\/ACCESS.2017.2762418","volume":"5","author":"C Yin","year":"2017","unstructured":"Yin C et al (2017) A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access 5:21954\u201321961","journal-title":"IEEE Access"},{"key":"4986_CR23","doi-asserted-by":"crossref","unstructured":"Ingre B, Yadav A (2015) Performance analysis of NSL-KDD dataset using ANN. In: 2015 international conference on signal processing and communication engineering systems. IEEE","DOI":"10.1109\/SPACES.2015.7058223"},{"key":"4986_CR24","doi-asserted-by":"publisher","first-page":"13965","DOI":"10.1007\/s10586-018-2158-3","volume":"22","author":"NS Murugan","year":"2019","unstructured":"Murugan NS, Devi GU (2019) Feature extraction using LR-PCA hybridization on twitter data and classification accuracy using machine learning algorithms. Cluster Comput 22:13965\u201313974","journal-title":"Cluster Comput"},{"key":"4986_CR25","unstructured":"https:\/\/www.unb.ca\/cic\/datasets\/NSL.html"},{"key":"4986_CR26","unstructured":"The UCI KDD Archive KDD\u201999 datasets. Irvine, CA, USA, 1999. http:\/\/kdd.ics.uci.edu\/databases\/kddcup99\/kddcup99.html"},{"key":"4986_CR27","doi-asserted-by":"publisher","first-page":"50850","DOI":"10.1109\/ACCESS.2018.2868993","volume":"6","author":"K Wu","year":"2018","unstructured":"Wu K, Chen Z, Li W (2018) A novel intrusion detection model for a massive network using convolutional neural networks. IEEE Access 6:50850\u201350859","journal-title":"IEEE Access"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-04986-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-04986-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-04986-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T18:12:22Z","timestamp":1662055942000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-04986-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,21]]},"references-count":27,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["4986"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-04986-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,21]]},"assertion":[{"value":"21 January 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2020","order":3,"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":"There is no conflict of interest from the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}