{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:33:54Z","timestamp":1785198834877,"version":"3.55.0"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T00:00:00Z","timestamp":1749254400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T00:00:00Z","timestamp":1749254400000},"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 Computing"],"DOI":"10.1007\/s10791-025-09632-z","type":"journal-article","created":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T05:33:55Z","timestamp":1749274435000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A multi-level intrusion detection system for industrial IoT using bowerbird courtship-inspired feature selection and hybrid data balancing"],"prefix":"10.1007","volume":"28","author":[{"given":"S Kumar Reddy","family":"Mallidi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajeswara Rao","family":"Ramisetty","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,7]]},"reference":[{"key":"9632_CR1","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.jnca.2015.11.016","volume":"60","author":"M Ahmed","year":"2016","unstructured":"Ahmed M, Naser Mahmood A, Hu J. A survey of network anomaly detection techniques. J Netw Comput Appl. 2016;60:19\u201331. https:\/\/doi.org\/10.1016\/j.jnca.2015.11.016.","journal-title":"J Netw Comput Appl"},{"key":"9632_CR2","doi-asserted-by":"publisher","first-page":"e523","DOI":"10.7717\/peerj-cs.523","volume":"7","author":"A Alhudhaif","year":"2021","unstructured":"Alhudhaif A. A novel multi-class imbalanced eeg signals classification based on the adaptive synthetic sampling (adasyn) approach. PeerJ Comput Sci. 2021;7:e523. https:\/\/doi.org\/10.7717\/peerj-cs.523.","journal-title":"PeerJ Comput Sci"},{"issue":"17","key":"9632_CR3","doi-asserted-by":"publisher","first-page":"7470","DOI":"10.3390\/s23177470","volume":"23","author":"B Alotaibi","year":"2023","unstructured":"Alotaibi B. A survey on industrial internet of things security: requirements, attacks, ai-based solutions, and edge computing opportunities. Sensors. 2023;23(17):7470. https:\/\/doi.org\/10.3390\/s23177470.","journal-title":"Sensors"},{"issue":"6","key":"9632_CR4","doi-asserted-by":"publisher","first-page":"3413","DOI":"10.1016\/j.jksuci.2021.01.014","volume":"34","author":"Asniar","year":"2022","unstructured":"Asniar A, Maulidevi NU, Surendro K. Smote-lof for noise identification in imbalanced data classification. J King Saud Univ Comput Inf Sci. 2022;34(6):3413\u201323. https:\/\/doi.org\/10.1016\/j.jksuci.2021.01.014.","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"9632_CR5","doi-asserted-by":"publisher","first-page":"52215","DOI":"10.1109\/ACCESS.2024.3386631","volume":"12","author":"MH Bhavsar","year":"2024","unstructured":"Bhavsar MH, Bekele YB, Roy K, et al. Fl-ids: federated learning-based intrusion detection system using edge devices for transportation iot. IEEE Access. 2024;12:52215\u201326. https:\/\/doi.org\/10.1109\/ACCESS.2024.3386631.","journal-title":"IEEE Access"},{"key":"9632_CR6","doi-asserted-by":"publisher","unstructured":"Bovenzi G, Aceto G, Ciuonzo D, et\u00a0al. A hierarchical hybrid intrusion detection approach in iot scenarios. In: GLOBECOM 2020 - 2020 IEEE Global Communications Conference. 2020; 1\u20137. https:\/\/doi.org\/10.1109\/GLOBECOM42002.2020.9348167.","DOI":"10.1109\/GLOBECOM42002.2020.9348167"},{"issue":"9","key":"9632_CR7","doi-asserted-by":"publisher","first-page":"6390","DOI":"10.1109\/TNNLS.2021.3136503","volume":"34","author":"D Dablain","year":"2023","unstructured":"Dablain D, Krawczyk B, Chawla NV. Deepsmote: fusing deep learning and smote for imbalanced data. IEEE Trans Neural Netw Learn Syst. 2023;34(9):6390\u2013404. https:\/\/doi.org\/10.1109\/TNNLS.2021.3136503.","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"9632_CR8","doi-asserted-by":"publisher","first-page":"9173291","DOI":"10.1155\/2022\/9173291","volume":"2022","author":"N Dat-Thinh","year":"2022","unstructured":"Dat-Thinh N, Xuan-Ninh H, Kim-Hung L. Midsiot: a multistage intrusion detection system for internet of things. Wirel Commun Mobile Comput. 2022;2022(1):9173291. https:\/\/doi.org\/10.1155\/2022\/9173291.","journal-title":"Wirel Commun Mobile Comput"},{"issue":"24","key":"9632_CR9","doi-asserted-by":"publisher","first-page":"14643","DOI":"10.1007\/s00521-024-09857-x","volume":"36","author":"AM Eid","year":"2024","unstructured":"Eid AM, Soudan B, Nassif AB, et al. Enhancing intrusion detection in iiot: optimized cnn model with multi-class smote balancing. Neural Comput Appl. 2024;36(24):14643\u201359. https:\/\/doi.org\/10.1007\/s00521-024-09857-x.","journal-title":"Neural Comput Appl"},{"issue":"8","key":"9632_CR10","doi-asserted-by":"publisher","first-page":"6882","DOI":"10.1109\/JIOT.2020.2970501","volume":"7","author":"M Eskandari","year":"2020","unstructured":"Eskandari M, Janjua ZH, Vecchio M, et al. Passban ids: an intelligent anomaly-based intrusion detection system for iot edge devices. IEEE Internet Things J. 2020;7(8):6882\u201397. https:\/\/doi.org\/10.1109\/JIOT.2020.2970501.","journal-title":"IEEE Internet Things J"},{"key":"9632_CR11","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2022.0130667","author":"AR Gad","year":"2022","unstructured":"Gad AR, Haggag M, Nashat AA, et al. A distributed intrusion detection system using machine learning for iot based on ton-iot dataset. Int J Adv Comput Sci Appl. 2022. https:\/\/doi.org\/10.14569\/IJACSA.2022.0130667.","journal-title":"Int J Adv Comput Sci Appl"},{"key":"9632_CR12","doi-asserted-by":"publisher","DOI":"10.34028\/iajit\/19\/5\/14","author":"A Guezzaz","year":"2022","unstructured":"Guezzaz A, Azrour M, Benkirane S, et al. A lightweight hybrid intrusion detection framework using machine learning for edge-based iiot security. Int Arab J Inf Technol. 2022. https:\/\/doi.org\/10.34028\/iajit\/19\/5\/14.","journal-title":"Int Arab J Inf Technol"},{"issue":"3","key":"9632_CR13","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.62527\/joiv.8.3.2283","volume":"8","author":"H Hairani","year":"2024","unstructured":"Hairani H, Widiyaningtyas T, Dwi Prasetya D. Addressing class imbalance of health data: a systematic literature review on modified synthetic minority oversampling technique (smote) strategies. JOIV Int J Inform Vis. 2024;8(3):1310\u20138. https:\/\/doi.org\/10.62527\/joiv.8.3.2283.","journal-title":"JOIV Int J Inform Vis"},{"issue":"4","key":"9632_CR14","doi-asserted-by":"publisher","first-page":"929","DOI":"10.26599\/TST.2023.9010033","volume":"29","author":"C Hazman","year":"2024","unstructured":"Hazman C, Guezzaz A, Benkirane S, et al. Enhanced ids with deep learning for iot-based smart cities security. Tsinghua Sci Technol. 2024;29(4):929\u201347. https:\/\/doi.org\/10.26599\/TST.2023.9010033.","journal-title":"Tsinghua Sci Technol"},{"issue":"3","key":"9632_CR15","doi-asserted-by":"publisher","first-page":"3512","DOI":"10.11591\/ijece.v14i3.pp3512-3521","volume":"14","author":"L Idouglid","year":"2024","unstructured":"Idouglid L, Tkatek S, Elfayq K, et al. Next-gen security in iiot: integrating intrusion detection systems with machine learning for industry 4.0 resilience. Int J Electr Comput Eng (IJECE). 2024;14(3):3512. https:\/\/doi.org\/10.11591\/ijece.v14i3.pp3512-3521.","journal-title":"Int J Electr Comput Eng (IJECE)"},{"key":"9632_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2024.103540","volume":"162","author":"D Javeed","year":"2024","unstructured":"Javeed D, Saeed MS, Adil M, et al. A federated learning-based zero trust intrusion detection system for internet of things. Ad Hoc Netw. 2024;162: 103540. https:\/\/doi.org\/10.1016\/j.adhoc.2024.103540.","journal-title":"Ad Hoc Netw"},{"issue":"9","key":"9632_CR17","doi-asserted-by":"publisher","first-page":"3211","DOI":"10.3390\/app10093211","volume":"10","author":"H Jeon","year":"2020","unstructured":"Jeon H, Oh S. Hybrid-recursive feature elimination for efficient feature selection. Appl Sci. 2020;10(9):3211. https:\/\/doi.org\/10.3390\/app10093211.","journal-title":"Appl Sci"},{"key":"9632_CR18","doi-asserted-by":"publisher","first-page":"7157","DOI":"10.1109\/ACCESS.2023.3237554","volume":"11","author":"J Jithish","year":"2023","unstructured":"Jithish J, Alangot B, Mahalingam N, et al. Distributed anomaly detection in smart grids: a federated learning-based approach. IEEE Access. 2023;11:7157\u201379. https:\/\/doi.org\/10.1109\/ACCESS.2023.3237554.","journal-title":"IEEE Access"},{"issue":"1","key":"9632_CR19","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1038\/s41598-023-50554-x","volume":"14","author":"M Karthikeyan","year":"2024","unstructured":"Karthikeyan M, Manimegalai D, RajaGopal K. Firefly algorithm based wsn-iot security enhancement with machine learning for intrusion detection. Sci Rep. 2024;14(1):231. https:\/\/doi.org\/10.1038\/s41598-023-50554-x.","journal-title":"Sci Rep"},{"key":"9632_CR20","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2024.3499942","author":"IA Khan","year":"2024","unstructured":"Khan IA, Pi D, Kamal S, et al. Federated-boosting: a distributed and dynamic boosting-powered cyber-attack detection scheme for security and privacy of consumer iot. IEEE Trans Consumer Electron. 2024. https:\/\/doi.org\/10.1109\/TCE.2024.3499942.","journal-title":"IEEE Trans Consumer Electron"},{"key":"9632_CR21","doi-asserted-by":"publisher","first-page":"102002","DOI":"10.1016\/j.inffus.2023.102002","volume":"101","author":"IA Khan","year":"2024","unstructured":"Khan IA, Razzak I, Pi D, et al. Fed-inforce-fusion: a federated reinforcement-based fusion model for security and privacy protection of iomt networks against cyber-attacks. Inf Fusion. 2024;101:102002. https:\/\/doi.org\/10.1016\/j.inffus.2023.102002.","journal-title":"Inf Fusion"},{"issue":"6","key":"9632_CR22","doi-asserted-by":"publisher","first-page":"3228","DOI":"10.1109\/JBHI.2024.3352013","volume":"28","author":"IA Khan","year":"2024","unstructured":"Khan IA, Razzak I, Pi D, et al. A novel collaborative sru network with dynamic behaviour aggregation, reduced communication overhead and explainable features. IEEE J Biomed Health Inform. 2024;28(6):3228\u201335. https:\/\/doi.org\/10.1109\/JBHI.2024.3352013.","journal-title":"IEEE J Biomed Health Inform"},{"key":"9632_CR23","doi-asserted-by":"publisher","first-page":"219709","DOI":"10.1109\/ACCESS.2020.3037359","volume":"8","author":"S Khanam","year":"2020","unstructured":"Khanam S, Ahmedy IB, Idna Idris MY, et al. A survey of security challenges, attacks taxonomy and advanced countermeasures in the internet of things. IEEE Access. 2020;8:219709\u201343. https:\/\/doi.org\/10.1109\/ACCESS.2020.3037359.","journal-title":"IEEE Access"},{"key":"9632_CR24","first-page":"252","volume-title":"Artificial intelligence algorithms for better decision-making","author":"G Khekare","year":"2024","unstructured":"Khekare G, Yenurkar G, Turukmane AV, et al. Artificial intelligence algorithms for better decision-making. Boca Raton, FL: CRC Press; 2024. p. 252\u201362."},{"key":"9632_CR25","doi-asserted-by":"publisher","DOI":"10.3390\/math12040571","author":"D Kilichev","year":"2024","unstructured":"Kilichev D, Turimov D, Kim W. Next-generation intrusion detection for iot evcs: integrating cnn, lstm, and gru models. Mathematics. 2024. https:\/\/doi.org\/10.3390\/math12040571.","journal-title":"Mathematics"},{"issue":"10","key":"9632_CR26","doi-asserted-by":"publisher","first-page":"9555","DOI":"10.1007\/s12652-020-02696-3","volume":"12","author":"P Kumar","year":"2021","unstructured":"Kumar P, Gupta GP, Tripathi R. A distributed ensemble design based intrusion detection system using fog computing to protect the internet of things networks. J Ambient Intell Humaniz Comput. 2021;12(10):9555\u201372. https:\/\/doi.org\/10.1007\/s12652-020-02696-3.","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"9632_CR27","doi-asserted-by":"publisher","first-page":"103784","DOI":"10.1016\/j.jnca.2023.103784","volume":"221","author":"S Latif","year":"2024","unstructured":"Latif S, Boulila W, Koubaa A, et al. Dtl-ids: an optimized intrusion detection framework using deep transfer learning and genetic algorithm. J Netw Comput Appl. 2024;221:103784. https:\/\/doi.org\/10.1016\/j.jnca.2023.103784.","journal-title":"J Netw Comput Appl"},{"issue":"8","key":"9632_CR28","doi-asserted-by":"publisher","first-page":"5615","DOI":"10.1109\/TII.2020.3023430","volume":"17","author":"B Li","year":"2021","unstructured":"Li B, Wu Y, Song J, et al. Deepfed: federated deep learning for intrusion detection in industrial cyber-physical systems. IEEE Trans Ind Inform. 2021;17(8):5615\u201324. https:\/\/doi.org\/10.1109\/TII.2020.3023430.","journal-title":"IEEE Trans Ind Inform"},{"issue":"7","key":"9632_CR29","doi-asserted-by":"publisher","first-page":"1120","DOI":"10.3390\/electronics9071120","volume":"9","author":"C Liang","year":"2020","unstructured":"Liang C, Shanmugam B, Azam S, et al. Intrusion detection system for the internet of things based on blockchain and multi-agent systems. Electronics. 2020;9(7):1120. https:\/\/doi.org\/10.3390\/electronics9071120.","journal-title":"Electronics"},{"key":"9632_CR30","doi-asserted-by":"publisher","first-page":"81736","DOI":"10.1109\/ACCESS.2024.3410046","volume":"12","author":"SS Mahadik","year":"2024","unstructured":"Mahadik SS, Pawar PM, Muthalagu R. Edge-federated learning-based intelligent intrusion detection system for heterogeneous internet of things. IEEE Access. 2024;12:81736\u201357. https:\/\/doi.org\/10.1109\/ACCESS.2024.3410046.","journal-title":"IEEE Access"},{"issue":"5","key":"9632_CR31","doi-asserted-by":"publisher","first-page":"1809","DOI":"10.3390\/s21051809","volume":"21","author":"P Malhotra","year":"2021","unstructured":"Malhotra P, Singh Y, Anand P, et al. Internet of things: evolution, concerns and security challenges. Sensors. 2021;21(5):1809. https:\/\/doi.org\/10.3390\/s21051809.","journal-title":"Sensors"},{"issue":"1","key":"9632_CR32","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1007\/s43926-025-00099-4","volume":"5","author":"SKR Mallidi","year":"2025","unstructured":"Mallidi SKR, Ramisetty RR. Advancements in training and deployment strategies for ai-based intrusion detection systems in iot: a systematic literature review. Discov Internet Things. 2025;5(1):8. https:\/\/doi.org\/10.1007\/s43926-025-00099-4.","journal-title":"Discov Internet Things"},{"issue":"1","key":"9632_CR33","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1007\/s10791-025-09497-2","volume":"28","author":"SKR Mallidi","year":"2025","unstructured":"Mallidi SKR, Ramisetty RR. Bowerbird courtship-inspired feature selection for efficient high-dimensional data analysis using a novel meta-heuristic. Discov Comput. 2025;28(1):6. https:\/\/doi.org\/10.1007\/s10791-025-09497-2.","journal-title":"Discov Comput"},{"key":"9632_CR34","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/3649406","author":"M Muntasir Nishat","year":"2022","unstructured":"Muntasir Nishat M, Faisal F, Jahan Ratul I, et al. A comprehensive investigation of the performances of different machine learning classifiers with smote-enn oversampling technique and hyperparameter optimization for imbalanced heart failure dataset. Sci Program. 2022. https:\/\/doi.org\/10.1155\/2022\/3649406.","journal-title":"Sci Program"},{"issue":"2","key":"9632_CR35","doi-asserted-by":"publisher","first-page":"100178","DOI":"10.1016\/j.hcc.2023.100178","volume":"4","author":"VO Nyangaresi","year":"2024","unstructured":"Nyangaresi VO, Yenurkar GK. Anonymity preserving lightweight authentication protocol for resource-limited wireless sensor networks. High-Confid Comput. 2024;4(2):100178. https:\/\/doi.org\/10.1016\/j.hcc.2023.100178.","journal-title":"High-Confid Comput"},{"issue":"1","key":"9632_CR36","doi-asserted-by":"publisher","first-page":"e13079","DOI":"10.1002\/eng2.13079","volume":"7","author":"VO Nyangaresi","year":"2025","unstructured":"Nyangaresi VO, AlRababah AA, Yenurkar GK, et al. Anonymous authentication scheme based on physically unclonable function and biometrics for smart cities. Eng Rep. 2025;7(1):e13079. https:\/\/doi.org\/10.1002\/eng2.13079.","journal-title":"Eng Rep"},{"key":"9632_CR37","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1016\/j.cose.2019.05.016","volume":"85","author":"R Patil","year":"2019","unstructured":"Patil R, Dudeja H, Modi C. Designing an efficient security framework for detecting intrusions in virtual network of cloud computing. Comput Secur. 2019;85:402\u201322. https:\/\/doi.org\/10.1016\/j.cose.2019.05.016.","journal-title":"Comput Secur"},{"issue":"3","key":"9632_CR38","doi-asserted-by":"publisher","first-page":"95","DOI":"10.4018\/IJISP.2020070106","volume":"14","author":"RKV Penmatsa","year":"2020","unstructured":"Penmatsa RKV, Kalidindi A, Mallidi SKR. Feature reduction and optimization of malware detection system using ant colony optimization and rough sets. Int J Inf Secur Priv (IJISP). 2020;14(3):95\u2013114. https:\/\/doi.org\/10.4018\/IJISP.2020070106.","journal-title":"Int J Inf Secur Priv (IJISP)"},{"key":"9632_CR39","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1049\/ntw2.12022","volume":"10","author":"RKV Penmatsa","year":"2021","unstructured":"Penmatsa RKV, Mallidi SKR, Jhansi KS, et al. Bat optimization algorithm for wrapper-based feature selection and performance improvement of android malware detection. IET Netw. 2021;10:131\u201340. https:\/\/doi.org\/10.1049\/ntw2.12022.","journal-title":"IET Netw"},{"key":"9632_CR40","doi-asserted-by":"publisher","first-page":"102324","DOI":"10.1016\/j.scs.2020.102324","volume":"61","author":"MA Rahman","year":"2020","unstructured":"Rahman MA, Asyhari AT, Leong L, et al. Scalable machine learning-based intrusion detection system for iot-enabled smart cities. Sustain Cities Soc. 2020;61:102324. https:\/\/doi.org\/10.1016\/j.scs.2020.102324.","journal-title":"Sustain Cities Soc"},{"key":"9632_CR41","doi-asserted-by":"publisher","first-page":"78621","DOI":"10.1109\/ACCESS.2021.3083638","volume":"9","author":"V Rupapara","year":"2021","unstructured":"Rupapara V, Rustam F, Shahzad HF, et al. Impact of smote on imbalanced text features for toxic comments classification using rvvc model. IEEE Access. 2021;9:78621\u201334. https:\/\/doi.org\/10.1109\/ACCESS.2021.3083638.","journal-title":"IEEE Access"},{"key":"9632_CR42","doi-asserted-by":"publisher","first-page":"100297","DOI":"10.1016\/j.sintl.2024.100297","volume":"6","author":"YK Saheed","year":"2025","unstructured":"Saheed YK, Omole AI, Sabit MO. Ga-madam-iiot: a new lightweight threats detection in the industrial iot via genetic algorithm with attention mechanism and lstm on multivariate time series sensor data. Sens Int. 2025;6:100297. https:\/\/doi.org\/10.1016\/j.sintl.2024.100297.","journal-title":"Sens Int"},{"issue":"1","key":"9632_CR43","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1007\/s10791-024-09495-w","volume":"28","author":"N Sambhe","year":"2025","unstructured":"Sambhe N, Yenurkar G, Kanase VV, et al. A comparative analysis using leach protocol to enhance energy efficiency in wireless sensor networks with harmony search algorithm. Discov Comput. 2025;28(1):2. https:\/\/doi.org\/10.1007\/s10791-024-09495-w.","journal-title":"Discov Comput"},{"key":"9632_CR44","doi-asserted-by":"publisher","first-page":"119462","DOI":"10.1109\/ACCESS.2023.3325929","volume":"11","author":"N Sarwar","year":"2023","unstructured":"Sarwar N, Bajwa IS, Hussain MZ, et al. Iot network anomaly detection in smart homes using machine learning. IEEE Access. 2023;11:119462\u201380. https:\/\/doi.org\/10.1109\/ACCESS.2023.3325929.","journal-title":"IEEE Access"},{"key":"9632_CR45","doi-asserted-by":"publisher","unstructured":"Soe YN, Santosa PI, Hartanto R. Ddos attack detection based on simple ann with smote for iot environment. In: 2019 Fourth International Conference on Informatics and Computing (ICIC). 2019; 1\u20135. https:\/\/doi.org\/10.1109\/ICIC47613.2019.8985853.","DOI":"10.1109\/ICIC47613.2019.8985853"},{"key":"9632_CR46","doi-asserted-by":"publisher","unstructured":"Sommer R, Paxson V. Outside the closed world: On using machine learning for network intrusion detection. In: Proceedings of the IEEE Symposium on Security and Privacy, IEEE. 2010. https:\/\/doi.org\/10.1109\/SP.2010.25.","DOI":"10.1109\/SP.2010.25"},{"key":"9632_CR47","doi-asserted-by":"publisher","unstructured":"Tavallaee M, Bagheri E, Lu W, et\u00a0al. A detailed analysis of the kdd cup 99 data set. In: 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications. 2009; 1\u20136. https:\/\/doi.org\/10.1109\/CISDA.2009.5356528.","DOI":"10.1109\/CISDA.2009.5356528"},{"issue":"12","key":"9632_CR48","doi-asserted-by":"publisher","first-page":"4102","DOI":"10.3390\/app10124102","volume":"10","author":"L Tawalbeh","year":"2020","unstructured":"Tawalbeh L, Muheidat F, Tawalbeh M, et al. Iot privacy and security: challenges and solutions. Appl Sci. 2020;10(12):4102. https:\/\/doi.org\/10.3390\/app10124102.","journal-title":"Appl Sci"},{"key":"9632_CR49","doi-asserted-by":"publisher","unstructured":"Umar BU, Muazu MB, Kolo JG, et\u00a0al. Epilepsy detection using artificial neural network and grasshopper optimization algorithm (goa). In: 2019 15th International Conference on Electronics, Computer and Computation (ICECCO). 2019; 1\u20136. https:\/\/doi.org\/10.1109\/ICECCO48375.2019.9043226.","DOI":"10.1109\/ICECCO48375.2019.9043226"},{"issue":"9","key":"9632_CR50","doi-asserted-by":"publisher","first-page":"15140","DOI":"10.1109\/JIOT.2023.3342638","volume":"11","author":"C Wang","year":"2024","unstructured":"Wang C, Xu D, Li Z, et al. Effective intrusion detection in highly imbalanced iot networks with lightweight s2cgan-ids. IEEE Internet Things J. 2024;11(9):15140\u201351. https:\/\/doi.org\/10.1109\/JIOT.2023.3342638.","journal-title":"IEEE Internet Things J"},{"key":"9632_CR51","doi-asserted-by":"publisher","first-page":"151525","DOI":"10.1109\/ACCESS.2019.2948095","volume":"7","author":"X Wang","year":"2019","unstructured":"Wang X, Guo B, Shen Y, et al. Input feature selection method based on feature set equivalence and mutual information gain maximization. IEEE Access. 2019;7:151525\u201338. https:\/\/doi.org\/10.1109\/ACCESS.2019.2948095.","journal-title":"IEEE Access"},{"issue":"1","key":"9632_CR52","doi-asserted-by":"publisher","first-page":"12718","DOI":"10.1038\/s41598-023-40036-5","volume":"13","author":"X Wang","year":"2023","unstructured":"Wang X, Ren J, Ren H, et al. Diabetes mellitus early warning and factor analysis using ensemble Bayesian networks with smote-enn and boruta. Sci Rep. 2023;13(1):12718. https:\/\/doi.org\/10.1038\/s41598-023-40036-5.","journal-title":"Sci Rep"},{"issue":"20","key":"9632_CR53","doi-asserted-by":"publisher","first-page":"4289","DOI":"10.3390\/electronics12204289","volume":"12","author":"B Xu","year":"2023","unstructured":"Xu B, Sun L, Mao X, et al. Iot intrusion detection system based on machine learning. Electronics. 2023;12(20):4289. https:\/\/doi.org\/10.3390\/electronics12204289.","journal-title":"Electronics"},{"issue":"6","key":"9632_CR54","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.3390\/electronics13061053","volume":"13","author":"S Yaras","year":"2024","unstructured":"Yaras S, Dener M. Iot-based intrusion detection system using new hybrid deep learning algorithm. Electronics. 2024;13(6):1053. https:\/\/doi.org\/10.3390\/electronics13061053.","journal-title":"Electronics"},{"key":"9632_CR55","unstructured":"Zolanvari M, Teixeira MA, Gupta L, et\u00a0al. WUSTL-IIOT-2021 Dataset for IIoT Cybersecurity Research. Washington University in St. Louis. 2021. http:\/\/www.cse.wustl.edu\/~jain\/iiot2\/index.html. Accessed 15 Jan 2023."},{"issue":"1","key":"9632_CR56","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/s40537-015-0013-4","volume":"2","author":"R Zuech","year":"2015","unstructured":"Zuech R, Khoshgoftaar TM, Wald R. Intrusion detection and big heterogeneous data: a survey. J Big Data. 2015;2(1):3. https:\/\/doi.org\/10.1186\/s40537-015-0013-4.","journal-title":"J Big Data"},{"issue":"3","key":"9632_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-015-0013-4","volume":"2","author":"R Zuech","year":"2015","unstructured":"Zuech R, Khoshgoftaar TM, Wald R. Intrusion detection and big heterogeneous data: a survey. J Big Data. 2015;2(3):1\u201341. https:\/\/doi.org\/10.1186\/s40537-015-0013-4.","journal-title":"J Big Data"}],"container-title":["Discover Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09632-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10791-025-09632-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09632-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T05:33:57Z","timestamp":1749274437000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10791-025-09632-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,7]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["9632"],"URL":"https:\/\/doi.org\/10.1007\/s10791-025-09632-z","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-6276232\/v1","asserted-by":"object"}]},"ISSN":["2948-2992"],"issn-type":[{"value":"2948-2992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,7]]},"assertion":[{"value":"21 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 June 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":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"109"}}