{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T07:56:07Z","timestamp":1743148567177,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031274398"},{"type":"electronic","value":"9783031274404"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-27440-4_48","type":"book-chapter","created":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T20:39:41Z","timestamp":1685479181000},"page":"501-511","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Ensemble Learning Based Big Data Classification for\u00a0Intrusion Detection"],"prefix":"10.1007","author":[{"given":"Kamel Yasmine","family":"Kamel","sequence":"first","affiliation":[]},{"given":"Farah","family":"Jemili","sequence":"additional","affiliation":[]},{"given":"Rahma","family":"Meddeb","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,31]]},"reference":[{"issue":"1","key":"48_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-020-00390-x","volume":"8","author":"S Bagui","year":"2021","unstructured":"Bagui, S., Li, K.: Resampling imbalanced data for network intrusion detection datasets. J. Big Data 8(1), 1\u201341 (2021). https:\/\/doi.org\/10.1186\/s40537-020-00390-x","journal-title":"J. Big Data"},{"key":"48_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.procs.2018.01.091","volume":"127","author":"M Belouch","year":"2018","unstructured":"Belouch, M., El Hadaj, S., Idhammad, M.: Performance evaluation of intrusion detection based on machine learning using apache spark. Procedia Comput. Sci. 127, 1\u20136 (2018)","journal-title":"Procedia Comput. Sci."},{"key":"48_CR3","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: 2016 International Conference on Advances in Computing, Communication, & Automation (ICACCA)(Spring), pp.\u00a01\u20136. IEEE (2016)","DOI":"10.1109\/ICACCA.2016.7578859"},{"key":"48_CR4","doi-asserted-by":"crossref","unstructured":"Chitrakar, R., Huang, C.: Anomaly based intrusion detection using hybrid learning approach of combining k-medoids clustering and Naive Bayes classification. In: 2012 8th International Conference on Wireless Communications, Networking and Mobile Computing, pp.\u00a01\u20135. IEEE (2012)","DOI":"10.1109\/WiCOM.2012.6478433"},{"key":"48_CR5","doi-asserted-by":"crossref","unstructured":"Chowdhury, R., Sen, S., Roy, A., Saha, B.: An optimal feature based network intrusion detection system using bagging ensemble method for real-time traffic analysis. Multimedia Tools and Applications, pp. 1\u201323 (2022)","DOI":"10.1007\/s11042-022-12330-3"},{"issue":"1","key":"48_CR6","first-page":"037","volume":"9","author":"M Dhakar","year":"2014","unstructured":"Dhakar, M., Tiwari, A.: A novel data mining based hybrid intrusion detection framework. J. Inf. Comput. Sci. 9(1), 037\u2013048 (2014)","journal-title":"J. Inf. Comput. Sci."},{"key":"48_CR7","volume":"50","author":"MA Ferrag","year":"2020","unstructured":"Ferrag, M.A., Maglaras, L., Moschoyiannis, S., Janicke, H.: Deep learning for cyber security intrusion detection: approaches, datasets, and comparative study. J. Inf. Secur. Appl. 50, 102419 (2020)","journal-title":"J. Inf. Secur. Appl."},{"key":"48_CR8","doi-asserted-by":"publisher","unstructured":"Hafsa, M., Jemili, F.: Comparative study between big data analysis techniques in intrusion detection. Big Data Cogn. Comput. 3, 1 (2019). https:\/\/doi.org\/10.3390\/bdcc3010001. https:\/\/www.mdpi.com\/2504-2289\/3\/1\/1","DOI":"10.3390\/bdcc3010001"},{"key":"48_CR9","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/OJCS.2021.3050917","volume":"2","author":"S Ho","year":"2021","unstructured":"Ho, S., Al Jufout, S., Dajani, K., Mozumdar, M.: A novel intrusion detection model for detecting known and innovative cyberattacks using convolutional neural network. IEEE Open J. Comput. Soc. 2, 14\u201325 (2021)","journal-title":"IEEE Open J. Comput. Soc."},{"key":"48_CR10","doi-asserted-by":"crossref","unstructured":"Huang, J., Kalbarczyk, Z., Nicol, D.M.: Knowledge discovery from big data for intrusion detection using IDA. In: 2014 IEEE International Congress on Big Data, pp. 760\u2013761. IEEE (2014)","DOI":"10.1109\/BigData.Congress.2014.111"},{"key":"48_CR11","doi-asserted-by":"crossref","unstructured":"Jemili, F., Zaghdoud, M., Ahmed, M.B.: Intrusion detection based on \u201chybrid\u201d propagation in Bayesian networks. In: 2009 IEEE International Conference on Intelligence and Security Informatics, pp. 137\u2013142. IEEE (2009)","DOI":"10.1109\/ISI.2009.5137285"},{"key":"48_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.107869","volume":"100","author":"IF Kilincer","year":"2022","unstructured":"Kilincer, I.F., Ertam, F., Sengur, A.: A comprehensive intrusion detection framework using boosting algorithms. Comput. Electr. Eng. 100, 107869 (2022)","journal-title":"Comput. Electr. Eng."},{"key":"48_CR13","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1016\/j.future.2019.05.041","volume":"100","author":"N Koroniotis","year":"2019","unstructured":"Koroniotis, N., Moustafa, N., Sitnikova, E., Turnbull, B.: Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-IoT dataset. Futur. Gener. Comput. Syst. 100, 779\u2013796 (2019)","journal-title":"Futur. Gener. Comput. Syst."},{"issue":"1","key":"48_CR14","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.eswa.2011.07.032","volume":"39","author":"Y Li","year":"2012","unstructured":"Li, Y., Xia, J., Zhang, S., Yan, J., Ai, X., Dai, K.: An efficient intrusion detection system based on support vector machines and gradually feature removal method. Expert Syst. Appl. 39(1), 424\u2013430 (2012)","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"48_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10723-021-09581-z","volume":"19","author":"M Mahdavisharif","year":"2021","unstructured":"Mahdavisharif, M., Jamali, S., Fotohi, R.: Big data-aware intrusion detection system in communication networks: a deep learning approach. J. Grid Comput. 19(4), 1\u201328 (2021)","journal-title":"J. Grid Comput."},{"key":"48_CR16","doi-asserted-by":"publisher","first-page":"22351","DOI":"10.1109\/ACCESS.2021.3056614","volume":"9","author":"ZK Maseer","year":"2021","unstructured":"Maseer, Z.K., Yusof, R., Bahaman, N., Mostafa, S.A., Foozy, C.F.M.: Benchmarking of machine learning for anomaly based intrusion detection systems in the cicids2017 dataset. IEEE Access 9, 22351\u201322370 (2021)","journal-title":"IEEE Access"},{"key":"48_CR17","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.procs.2019.09.162","volume":"159","author":"R Meddeb","year":"2019","unstructured":"Meddeb, R., Jemili, F., Triki, B., Korbaa, O.: Anomaly-based behavioral detection in mobile Ad-Hoc networks. Procedia Comput. Sci. 159, 77\u201386 (2019)","journal-title":"Procedia Comput. Sci."},{"key":"48_CR18","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.protcy.2012.05.017","volume":"4","author":"S Mukherjee","year":"2012","unstructured":"Mukherjee, S., Sharma, N.: Intrusion detection using Naive Bayes classifier with feature reduction. Procedia Technol. 4, 119\u2013128 (2012)","journal-title":"Procedia Technol."},{"key":"48_CR19","doi-asserted-by":"crossref","unstructured":"Primartha, R., Tama, B.A.: Anomaly detection using random forest: a performance revisited. In: 2017 International Conference on Data and Software Engineering (ICoDSE), pp.\u00a01\u20136. IEEE (2017)","DOI":"10.1109\/ICODSE.2017.8285847"},{"key":"48_CR20","doi-asserted-by":"crossref","unstructured":"Rajagopal, S., Kundapur, P.P., Hareesha, K.S.: A stacking ensemble for network intrusion detection using heterogeneous datasets. Secur. Commun. Netw. 2020, 4586875 (2020)","DOI":"10.1155\/2020\/4586875"},{"key":"48_CR21","doi-asserted-by":"crossref","unstructured":"Rashid, M., Kamruzzaman, J., Imam, T., Wibowo, S., Gordon, S.: A tree-based stacking ensemble technique with feature selection for network intrusion detection. Applied Intelligence, pp. 1\u201314 (2022)","DOI":"10.1007\/s10489-021-02968-1"},{"key":"48_CR22","doi-asserted-by":"crossref","unstructured":"Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: ICISSP, vol. 1, pp. 108\u2013116 (2018)","DOI":"10.5220\/0006639801080116"},{"key":"48_CR23","doi-asserted-by":"crossref","unstructured":"Sharma, R., Sharma, P., Mishra, P., Pilli, E.S.: Towards mapreduce based classification approaches for intrusion detection. In: 2016 6th International Conference-Cloud System and Big Data Engineering (Confluence), pp. 361\u2013367. IEEE (2016)","DOI":"10.1109\/CONFLUENCE.2016.7508144"},{"issue":"10","key":"48_CR24","doi-asserted-by":"publisher","first-page":"2509","DOI":"10.3390\/en13102509","volume":"13","author":"K Shaukat","year":"2020","unstructured":"Shaukat, K., et al.: Performance comparison and current challenges of using machine learning techniques in cybersecurity. Energies 13(10), 2509 (2020)","journal-title":"Energies"},{"key":"48_CR25","doi-asserted-by":"crossref","unstructured":"Vimalkumar, K., Radhika, N.: A big data framework for intrusion detection in smart grids using apache spark. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 198\u2013204. IEEE (2017)","DOI":"10.1109\/ICACCI.2017.8125840"},{"issue":"1","key":"48_CR26","first-page":"24","volume":"7","author":"L Wang","year":"2017","unstructured":"Wang, L., Jones, R.: Big data analytics for network intrusion detection: a survey. Int. J. Netw. Commun. 7(1), 24\u201331 (2017)","journal-title":"Int. J. Netw. Commun."}],"container-title":["Lecture Notes in Networks and Systems","Intelligent Systems Design and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-27440-4_48","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T20:48:07Z","timestamp":1685479687000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-27440-4_48"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031274398","9783031274404"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-27440-4_48","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISDA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Systems Design and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isda2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.mirlabs.net\/isda22\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}