{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T22:26:49Z","timestamp":1773872809766,"version":"3.50.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Internet Things"],"DOI":"10.1007\/s43926-025-00199-1","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T10:15:28Z","timestamp":1760523328000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Twined ensemble framework for network security: integrating Random Forest, AdaBoost, and Gradient Boosting for enhanced intrusion detection"],"prefix":"10.1007","volume":"5","author":[{"given":"C. Kishor Kumar","family":"Reddy","sequence":"first","affiliation":[]},{"given":"Pulakurthi Anaghaa","family":"Reddy","sequence":"additional","affiliation":[]},{"given":"Pulakurthi Satyanarayana","family":"Reddy","sequence":"additional","affiliation":[]},{"given":"Mohammed","family":"Shuaib","sequence":"additional","affiliation":[]},{"given":"Shadab","family":"Alam","sequence":"additional","affiliation":[]},{"given":"Sadaf","family":"Ahmad","sequence":"additional","affiliation":[]},{"given":"A.","family":"Rajaram","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,15]]},"reference":[{"key":"199_CR1","unstructured":"2016 IEEE international conference on recent trends in electronics, information & communication technology (RTEICT). IEEE; 2016."},{"issue":"3","key":"199_CR2","doi-asserted-by":"publisher","first-page":"4025","DOI":"10.32604\/cmc.2023.043752","volume":"77","author":"M Zakariah","year":"2023","unstructured":"Zakariah M, AlQahtani SA, Alawwad AM, Alotaibi AA. Intrusion detection system with customizedmachine learning techniques for NSL-KDD dataset. Comput Mater Contin. 2023;77(3):4025\u201354. https:\/\/doi.org\/10.32604\/cmc.2023.043752.","journal-title":"Comput Mater Contin"},{"issue":"1","key":"199_CR3","doi-asserted-by":"publisher","first-page":"118","DOI":"10.24191\/jcrinn.v7i1.274","volume":"7","author":"S Rastogi","year":"2022","unstructured":"Rastogi S, Shrotriya A, Singh MK, Potukuchi RV. An analysis of intrusion detection classification using supervised machine learning algorithms on NSL-KDD dataset. J Comput Res Innov. 2022;7(1):118\u201330. https:\/\/doi.org\/10.24191\/jcrinn.v7i1.274.","journal-title":"J Comput Res Innov"},{"issue":"5","key":"199_CR4","doi-asserted-by":"publisher","first-page":"419","DOI":"10.3103\/S0146411619050043","volume":"53","author":"N Bindra","year":"2019","unstructured":"Bindra N, Sood M. Detecting DDoS attacks using machine learning techniques and contemporary intrusion detection dataset. Autom Control Comput Sci. 2019;53(5):419\u201328. https:\/\/doi.org\/10.3103\/S0146411619050043.","journal-title":"Autom Control Comput Sci"},{"issue":"2","key":"199_CR5","doi-asserted-by":"publisher","first-page":"465","DOI":"10.17798\/bitlisfen.1240469","volume":"12","author":"F T\u00fcrk","year":"2023","unstructured":"T\u00fcrk F. Analysis of intrusion detection systems in UNSW-NB15 and NSL-KDD datasets with machine learning algorithms. Bitlis Eren \u00dcniversitesi Fen Bilimleri Dergisi. 2023;12(2):465\u201377. https:\/\/doi.org\/10.17798\/bitlisfen.1240469.","journal-title":"Bitlis Eren \u00dcniversitesi Fen Bilimleri Dergisi"},{"issue":"2","key":"199_CR6","doi-asserted-by":"publisher","first-page":"80","DOI":"10.20895\/inista.v6i2.1389","volume":"6","author":"Y Yuliana","year":"2024","unstructured":"Yuliana Y, Supriyadi DH, Fahlevi MR, Arisagas MR. Analysis of NSL-KDD for the implementation of machine learning in network intrusion detection system. J Inform Inf Syst Softw Eng Appl INISTA. 2024;6(2):80\u20139. https:\/\/doi.org\/10.20895\/inista.v6i2.1389.","journal-title":"J Inform Inf Syst Softw Eng Appl INISTA"},{"issue":"1","key":"199_CR7","doi-asserted-by":"publisher","first-page":"46","DOI":"10.22937\/IJCSNS.2023.23.1.7","volume":"23","author":"Z Good","year":"2023","unstructured":"Good Z, et al. Comparative analysis of machine learning techniques for IoT anomaly detection using the NSL-KDD dataset. IJCSNS Int J Comput Sci Netw Secur. 2023;23(1):46. https:\/\/doi.org\/10.22937\/IJCSNS.2023.23.1.7.","journal-title":"IJCSNS Int J Comput Sci Netw Secur"},{"key":"199_CR8","doi-asserted-by":"publisher","unstructured":"Juvonen A, Hamalainen T. An efficient network log anomaly detection system using random projection dimensionality reduction. In 2014 6th international conference on new technologies, mobility and security\u2014proceedings of NTMS 2014 conference and workshops, IEEE Computer Society; 2014. https:\/\/doi.org\/10.1109\/NTMS.2014.6814006.","DOI":"10.1109\/NTMS.2014.6814006"},{"key":"199_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2024.03.285","author":"AD Vibhute","year":"2024","unstructured":"Vibhute AD, Patil CH, Mane AV, Kale KV. Towards detection of network anomalies using machine learning algorithms on the NSL-KDD benchmark datasets. Procedia Comput Sci. 2024. https:\/\/doi.org\/10.1016\/j.procs.2024.03.285.","journal-title":"Procedia Comput Sci"},{"key":"199_CR10","unstructured":"Proceedings of the international conference on electronics and sustainable communication systems (ICESC 2020): 02\u201304, July 2020. IEEE; 2020."},{"key":"199_CR11","doi-asserted-by":"publisher","unstructured":"Salo F, Injadat MN, Moubayed A, Nassif AB, Essex A. Clustering enabled classification using ensemble feature selection for intrusion detection. In: 2019 international conference on computing, networking and communications, ICNC 2019, Institute of Electrical and Electronics Engineers Inc.;2019. p. 276\u201381. https:\/\/doi.org\/10.1109\/ICCNC.2019.8685636.","DOI":"10.1109\/ICCNC.2019.8685636"},{"issue":"12","key":"199_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5120\/ijca2016910764","volume":"150","author":"K Kumar","year":"2016","unstructured":"Kumar K, Singh J. Network intrusion detection with feature selection techniques using machine-learning algorithms. Int J Comput Appl. 2016;150(12):1\u201313. https:\/\/doi.org\/10.5120\/ijca2016910764.","journal-title":"Int J Comput Appl"},{"key":"199_CR13","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-023-00697-5","author":"A Shiravani","year":"2023","unstructured":"Shiravani A, Sadreddini MH, Nahook HN. Network intrusion detection using data dimensions reduction techniques. J Big Data. 2023. https:\/\/doi.org\/10.1186\/s40537-023-00697-5.","journal-title":"J Big Data"},{"issue":"4","key":"199_CR14","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.1109\/TNSM.2020.3016246","volume":"17","author":"B Molina-Coronado","year":"2020","unstructured":"Molina-Coronado B, Mori U, Mendiburu A, Miguel-Alonso J. Survey of network intrusion detection methods from the perspective of the knowledge discovery in databases process. IEEE Trans Netw Serv Manag. 2020;17(4):2451\u201379. https:\/\/doi.org\/10.1109\/TNSM.2020.3016246.","journal-title":"IEEE Trans Netw Serv Manag"},{"key":"199_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.07.080","author":"BM Serinelli","year":"2020","unstructured":"Serinelli BM, Collen A, Nijdam NA. Training guidance with KDD cup 1999 and NSL-KDD data sets of ANIDINR: anomaly-based network intrusion detection system. Procedia Comput Sci. 2020. https:\/\/doi.org\/10.1016\/j.procs.2020.07.080.","journal-title":"Procedia Comput Sci"},{"key":"199_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2016.06.016","author":"MC Belavagi","year":"2016","unstructured":"Belavagi MC, Muniyal B. Performance evaluation of supervised machine learning algorithms for intrusion detection. Procedia Comput Sci. 2016. https:\/\/doi.org\/10.1016\/j.procs.2016.06.016.","journal-title":"Procedia Comput Sci"},{"key":"199_CR17","doi-asserted-by":"publisher","unstructured":"Haricharan MG, Govind SP, Kumar CNSV. An enhanced network security using machine learning and behavioral analysis. In: 2023 international conference for advancement in technology (ICONAT); 2023. p. 1\u20135. https:\/\/doi.org\/10.1109\/ICONAT57137.2023.10080157.","DOI":"10.1109\/ICONAT57137.2023.10080157"},{"key":"199_CR18","doi-asserted-by":"publisher","unstructured":"Aboueata N, Alrasbi S, Erbad A, Kassler A, Bhamare D. Supervised machine learning techniques for efficient network intrusion detection. In: Proceedings\u2014international conference on computer communications and networks, ICCCN, Institute of Electrical and Electronics Engineers Inc.; 2019. https:\/\/doi.org\/10.1109\/ICCCN.2019.8847179.","DOI":"10.1109\/ICCCN.2019.8847179"},{"key":"199_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.csa.2023.100033","author":"F Nabi","year":"2024","unstructured":"Nabi F, Zhou X. Enhancing intrusion detection systems through dimensionality reduction: a comparative study of machine learning techniques for cyber security. Cyber Secur Appl. 2024. https:\/\/doi.org\/10.1016\/j.csa.2023.100033.","journal-title":"Cyber Secur Appl"},{"key":"199_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.03.330","author":"A Thakkar","year":"2020","unstructured":"Thakkar A, Lohiya R. A review of the advancement in intrusion detection datasets. Procedia Comput Sci. 2020. https:\/\/doi.org\/10.1016\/j.procs.2020.03.330.","journal-title":"Procedia Comput Sci"},{"key":"199_CR21","doi-asserted-by":"publisher","unstructured":"Tavallaee M, Bagheri E, Lu W, Ghorbani AA. A detailed analysis of the KDD CUP 99 data set. In: IEEE symposium on computational intelligence for security and defense applications, CISDA 2009; 2009. https:\/\/doi.org\/10.1109\/CISDA.2009.5356528.","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"199_CR22","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1007\/978-981-15-4032-5_52","volume-title":"Advances in intelligent systems and computing","author":"I Kumar","year":"2020","unstructured":"Kumar I, Mohd N, Bhatt C, Sharma SK. Development of IDS using supervised machine learning. In: Advances in intelligent systems and computing. Springer; 2020. p. 565\u201377. https:\/\/doi.org\/10.1007\/978-981-15-4032-5_52."},{"key":"199_CR23","doi-asserted-by":"crossref","unstructured":"Tauscher Z et al. Learning to detect: a data-driven approach for network intrusion detection. In: 2021 IEEE international performance, computing, and communications conference (IPCCC). IEEE; 2021.","DOI":"10.1109\/IPCCC51483.2021.9679415"},{"issue":"C","key":"199_CR24","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/J.PROCS.2022.03.029","volume":"201","author":"EE Abdallah","year":"2022","unstructured":"Abdallah EE, Eleisah W, Otoom AF. Intrusion detection systems using supervised machine learning techniques: a survey. Procedia Comput Sci. 2022;201(C):205\u201312. https:\/\/doi.org\/10.1016\/J.PROCS.2022.03.029.","journal-title":"Procedia Comput Sci"},{"key":"199_CR25","doi-asserted-by":"publisher","first-page":"02003","DOI":"10.1051\/itmconf\/20224602003","volume":"46","author":"R Tahri","year":"2022","unstructured":"Tahri R, Balouki Y, Jarrar A, Lasbahani A. Intrusion detection system using machine learning algorithms. ITM Web Conf. 2022;46:02003. https:\/\/doi.org\/10.1051\/itmconf\/20224602003.","journal-title":"ITM Web Conf"},{"issue":"6","key":"199_CR26","doi-asserted-by":"publisher","first-page":"251","DOI":"10.29130\/dubited.1018229","volume":"9","author":"FM \u00c7imen","year":"2021","unstructured":"\u00c7imen FM, S\u00f6nmez Y, Ilba\u015f M. Performance analysis of machine learning algorithms in intrusion detection systems. D\u00fczce \u00dcniversitesi Bilim ve Teknoloji Dergisi. 2021;9(6):251\u20138. https:\/\/doi.org\/10.29130\/dubited.1018229.","journal-title":"D\u00fczce \u00dcniversitesi Bilim ve Teknoloji Dergisi"},{"issue":"3","key":"199_CR27","doi-asserted-by":"publisher","first-page":"193","DOI":"10.12913\/22998624\/149934","volume":"16","author":"YS Almutairi","year":"2022","unstructured":"Almutairi YS, Alhazmi B, Munshi AA. Network intrusion detection using machine learning techniques. Adv Sci Technol Res J. 2022;16(3):193\u2013206. https:\/\/doi.org\/10.12913\/22998624\/149934.","journal-title":"Adv Sci Technol Res J"},{"key":"199_CR28","doi-asserted-by":"publisher","DOI":"10.1186\/s13677-024-00685-x","author":"M Sajid","year":"2024","unstructured":"Sajid M, et al. Enhancing intrusion detection: a hybrid machine and deep learning approach. J Cloud Comput. 2024. https:\/\/doi.org\/10.1186\/s13677-024-00685-x.","journal-title":"J Cloud Comput"}],"container-title":["Discover Internet of Things"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43926-025-00199-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s43926-025-00199-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s43926-025-00199-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T10:15:32Z","timestamp":1760523332000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s43926-025-00199-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,15]]},"references-count":28,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["199"],"URL":"https:\/\/doi.org\/10.1007\/s43926-025-00199-1","relation":{},"ISSN":["2730-7239"],"issn-type":[{"value":"2730-7239","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,15]]},"assertion":[{"value":"15 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving human and \/or animals"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Mohammed Shuaib declares they are an Editorial Board Member of Discover Internet of Things and confirms that they were not involved in the handling or decision-making of their own submission.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"107"}}