{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T16:33:32Z","timestamp":1785602012616,"version":"3.56.0"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T00:00:00Z","timestamp":1745366400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T00:00:00Z","timestamp":1745366400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["IS23054"],"award-info":[{"award-number":["IS23054"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-07255-1","type":"journal-article","created":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T18:10:56Z","timestamp":1745431856000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Empirical evaluation of ensemble learning and hybrid CNN-LSTM for IoT threat detection on heterogeneous datasets"],"prefix":"10.1007","volume":"81","author":[{"given":"Ahsan","family":"Nazir","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingsha","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nafei","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahsan","family":"Wajahat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fahim","family":"Ullah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sirajuddin","family":"Qureshi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Salman","family":"Pathan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,23]]},"reference":[{"issue":"2","key":"7255_CR1","doi-asserted-by":"publisher","first-page":"732","DOI":"10.18421\/TEM122-17","volume":"12","author":"V Voronkova","year":"2023","unstructured":"Voronkova V, Nikitenko V, Oleksenko R, Andriukaitiene R, Kharchenko J, Kliuienko E (2023) Digital technology evolution of the industrial revolution from 4g to 5g in the context of the challenges of digital globalization. TEM J 12(2):732\u2013742","journal-title":"TEM J"},{"key":"7255_CR2","doi-asserted-by":"publisher","first-page":"100467","DOI":"10.1016\/j.cosrev.2022.100467","volume":"44","author":"E Schiller","year":"2022","unstructured":"Schiller E, Aidoo A, Fuhrer J, Stahl J, Zi\u00f6rjen M, Stiller B (2022) Landscape of iot security. Comput Sci Rev 44:100467","journal-title":"Comput Sci Rev"},{"key":"7255_CR3","doi-asserted-by":"publisher","first-page":"101820","DOI":"10.1016\/j.jksuci.2023.101820","volume":"35","author":"A Nazir","year":"2023","unstructured":"Nazir A, He J, Zhu N, Wajahat A, Ma X, Ullah F, Qureshi S, Pathan MS (2023) Advancing iot security: a systematic review of machine learning approaches for the detection of iot botnets. J King Saud Univ Comput Inf Sci 35:101820","journal-title":"J King Saud Univ Comput Inf Sci"},{"issue":"3","key":"7255_CR4","doi-asserted-by":"publisher","first-page":"493","DOI":"10.3390\/jcp3030025","volume":"3","author":"A Giannaros","year":"2023","unstructured":"Giannaros A, Karras A, Theodorakopoulos L, Karras C, Kranias P, Schizas N, Kalogeratos G, Tsolis D (2023) Autonomous vehicles: sophisticated attacks, safety issues, challenges, open topics, blockchain, and future directions. J Cybersecur Priv 3(3):493\u2013543","journal-title":"J Cybersecur Priv"},{"key":"7255_CR5","doi-asserted-by":"publisher","first-page":"101939","DOI":"10.1016\/j.jksuci.2024.101939","volume":"36","author":"A Nazir","year":"2024","unstructured":"Nazir A, He J, Zhu N, Wajahat A, Ullah F, Qureshi S, Ma X, Pathan MS (2024) Collaborative threat intelligence: enhancing iot security through blockchain and machine learning integration. J King Saud Univ Comput Inf Sci 36:101939","journal-title":"J King Saud Univ Comput Inf Sci"},{"issue":"16","key":"7255_CR6","doi-asserted-by":"publisher","first-page":"7194","DOI":"10.3390\/s23167194","volume":"23","author":"R Chataut","year":"2023","unstructured":"Chataut R, Phoummalayvane A, Akl R (2023) Unleashing the power of iot: a comprehensive review of iot applications and future prospects in healthcare, agriculture, smart homes, smart cities, and industry 4.0. Sensors 23(16):7194","journal-title":"Sensors"},{"key":"7255_CR7","doi-asserted-by":"crossref","unstructured":"Najat R, Haitam E, Jaafar A (2023) Comparative study of the security analysis of iot systems using attack trees algorithm. In: E3S Web of Conferences, vol 412, pp 01087. EDP Sciences","DOI":"10.1051\/e3sconf\/202341201087"},{"key":"7255_CR8","doi-asserted-by":"publisher","first-page":"6738","DOI":"10.1109\/TIFS.2024.3423724","volume":"19","author":"A Shahidinejad","year":"2024","unstructured":"Shahidinejad A, Abawajy J, Huda S (2024) Highly-secure yet efficient blockchain-based crl-free key management protocol for iot-enabled smart grid environments. IEEE Trans Inf Forensics Secur 19:6738\u20136750. https:\/\/doi.org\/10.1109\/TIFS.2024.3423724","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"7","key":"7255_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3645087","volume":"56","author":"A Shahidinejad","year":"2024","unstructured":"Shahidinejad A, Abawajy J (2024) An all-inclusive taxonomy and critical review of blockchain-assisted authentication and session key generation protocols for iot. ACM Comput Surv 56(7):1\u201338. https:\/\/doi.org\/10.1145\/3645087","journal-title":"ACM Comput Surv"},{"key":"7255_CR10","doi-asserted-by":"publisher","first-page":"1879","DOI":"10.1007\/s11277-024-11366-y","volume":"136","author":"P Thakur","year":"2024","unstructured":"Thakur P, Kansal V, Rishiwal V (2024) Hybrid deep learning approach based on lstm and cnn for malware detection. Wirel Pers Commun 136:1879\u20131901. https:\/\/doi.org\/10.1007\/s11277-024-11366-y","journal-title":"Wirel Pers Commun"},{"key":"7255_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/s44163-024-00207-3","author":"S Rashmi","year":"2024","unstructured":"Rashmi S, Srinath S, Rakshitha R, Poornima BV (2024) Ensemble learning methods with single and multi-model deep learning approaches for cephalometric landmark annotation. Discov Artif Intell. https:\/\/doi.org\/10.1007\/s44163-024-00207-3","journal-title":"Discov Artif Intell"},{"issue":"18","key":"7255_CR12","doi-asserted-by":"publisher","first-page":"7856","DOI":"10.3390\/s23187856","volume":"23","author":"UK Lilhore","year":"2023","unstructured":"Lilhore UK, Manoharan P, Simaiya S, Alroobaea R, Alsafyani M, Baqasah AM et al (2023) Hidm: hybrid intrusion detection model for industry 4.0 networks using an optimized cnn-lstm with transfer learning. Sensors 23(18):7856","journal-title":"Sensors"},{"key":"7255_CR13","doi-asserted-by":"crossref","unstructured":"Xu X, Sun J, Wang C, Zou B (2022) A novel hybrid cnn-lstm compensation model against dos attacks in power system state estimation. Neural Process Lett 1\u201325","DOI":"10.1007\/s11063-021-10696-3"},{"key":"7255_CR14","doi-asserted-by":"crossref","unstructured":"Almarshdi R, Nassef L, Fadel E, Alowidi N (2023) Hybrid deep learning based attack detection for imbalanced data classification. Intell Autom Soft Comput 35(1)","DOI":"10.32604\/iasc.2023.026799"},{"key":"7255_CR15","unstructured":"Garcia S, Parmisano A, Erquiaga MJ (2020) Iot-23: a labeled dataset with malicious and benign iot network traffic. Dataset"},{"key":"7255_CR16","doi-asserted-by":"publisher","DOI":"10.24432\/C5RC8J","author":"Y Meidan","year":"2018","unstructured":"Meidan Y, Bohadana M, Mathov Y, Mirsky Y, Breitenbacher D, Asaf A, Shabtai A (2018) Detection of IoT Botnet attacks N-BaIoT. UCI Mach Learn Repos. https:\/\/doi.org\/10.24432\/C5RC8J","journal-title":"UCI Mach Learn Repos"},{"key":"7255_CR17","doi-asserted-by":"crossref","unstructured":"Sharafaldin I, Lashkari A, Ghorbani A (2018) Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: ICISSP","DOI":"10.5220\/0006639801080116"},{"issue":"1","key":"7255_CR18","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1007\/s11036-022-01937-3","volume":"28","author":"IH Sarker","year":"2023","unstructured":"Sarker IH, Khan AI, Abushark YB, Alsolami F (2023) Internet of things (iot) security intelligence: a comprehensive overview, machine learning solutions and research directions. Mob Netw Appl 28(1):296\u2013312","journal-title":"Mob Netw Appl"},{"key":"7255_CR19","doi-asserted-by":"crossref","unstructured":"Anbazhagan A, Guru K, Masood G, Mandaviya M, Dhiman V, Naved M (2022) Critically analyzing the concept of internet of things (iot) and how it impacts employee and organizational performance. In: Proceedings of Second International Conference in Mechanical and Energy Technology: ICMET 2021, India, pp 121\u2013130. Springer","DOI":"10.1007\/978-981-19-0108-9_13"},{"key":"7255_CR20","doi-asserted-by":"crossref","unstructured":"Bouzidi M, Gupta N, Cheikh FA, Shalaginov A, Derawi M (2022) A novel architectural framework on iot ecosystem, security aspects and mechanisms: a comprehensive survey. IEEE Access","DOI":"10.1109\/ACCESS.2022.3207472"},{"key":"7255_CR21","doi-asserted-by":"publisher","first-page":"45893","DOI":"10.1109\/ACCESS.2022.3169137","volume":"10","author":"TH Szymanski","year":"2022","unstructured":"Szymanski TH (2022) The \u201ccyber security via determinism\u2019\u2019 paradigm for a quantum safe zero trust deterministic internet of things (iot). IEEE Access 10:45893\u201345930","journal-title":"IEEE Access"},{"key":"7255_CR22","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1007\/s42488-020-00030-2","volume":"2","author":"S Panchiwala","year":"2020","unstructured":"Panchiwala S, Shah M (2020) A comprehensive study on critical security issues and challenges of the iot world. J Data Inf Manag 2:257\u2013278","journal-title":"J Data Inf Manag"},{"issue":"3","key":"7255_CR23","doi-asserted-by":"publisher","first-page":"1686","DOI":"10.1109\/COMST.2020.2986444","volume":"22","author":"F Hussain","year":"2020","unstructured":"Hussain F, Hussain R, Hassan SA, Hossain E (2020) Machine learning in iot security: current solutions and future challenges. IEEE Commun Surv Tutor 22(3):1686\u20131721","journal-title":"IEEE Commun Surv Tutor"},{"key":"7255_CR24","doi-asserted-by":"crossref","unstructured":"Bajao NA, Sarucam J-A (2023) Threats detection in the internet of things using convolutional neural networks, long short-term memory, and gated recurrent units. Mesop J Cybersecur 22\u201329","DOI":"10.58496\/MJCS\/2023\/005"},{"key":"7255_CR25","doi-asserted-by":"crossref","unstructured":"Saba T (2020) Intrusion detection in smart city hospitals using ensemble classifiers. In: 2020 13th International Conference on Developments in eSystems Engineering (DeSE), pp 418\u2013422. IEEE","DOI":"10.1109\/DeSE51703.2020.9450247"},{"key":"7255_CR26","doi-asserted-by":"crossref","unstructured":"Booij TM, et\u00a0al. (2021) Ton_iot: the role of heterogeneity and the need for standardization of features and attack types in iot network intrusion datasets. IEEE Internet Things J","DOI":"10.1109\/JIOT.2021.3085194"},{"issue":"8","key":"7255_CR27","doi-asserted-by":"publisher","first-page":"6591","DOI":"10.1109\/JIOT.2021.3055937","volume":"8","author":"KLK Sudheera","year":"2021","unstructured":"Sudheera KLK et al (2021) Adept: detection and identification of correlated attack stages in iot networks. IEEE Internet Things J 8(8):6591\u20136607","journal-title":"IEEE Internet Things J"},{"key":"7255_CR28","doi-asserted-by":"crossref","unstructured":"Umair M, Tan W-H, Foo Y-L (2023) Efficient malware classification with spiking neural networks: a case study on n-baiot dataset. 2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN)","DOI":"10.1109\/ICUFN57995.2023.10200941"},{"key":"7255_CR29","doi-asserted-by":"crossref","unstructured":"Saurabh K, et\u00a0al. (2022) Ganibot: a network flow based semi supervised generative adversarial networks model for iot botnets detection. 2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","DOI":"10.1109\/COINS54846.2022.9854947"},{"key":"7255_CR30","doi-asserted-by":"crossref","unstructured":"Ahmad R, Alsmadi I (2021) Machine learning approaches to iot security: a systematic literature review. Internet Things 100365","DOI":"10.1016\/j.iot.2021.100365"},{"key":"7255_CR31","doi-asserted-by":"crossref","unstructured":"Sahu AK, et\u00a0al. (2021) Internet of things attack detection using hybrid deep learning model. Computer Communications","DOI":"10.1016\/j.comcom.2021.05.024"},{"issue":"12","key":"7255_CR32","doi-asserted-by":"publisher","first-page":"2006","DOI":"10.3390\/electronics9122006","volume":"9","author":"M Al-Zewairi","year":"2020","unstructured":"Al-Zewairi M, Almajali S, Ayyash M (2020) Unknown security attack detection using shallow and deep ann classifiers. Electronics 9(12):2006","journal-title":"Electronics"},{"issue":"16","key":"7255_CR33","doi-asserted-by":"publisher","first-page":"4583","DOI":"10.3390\/s20164583","volume":"20","author":"V Dutta","year":"2020","unstructured":"Dutta V et al (2020) A deep learning ensemble for network anomaly and cyber-attack detection. Sensors 20(16):4583","journal-title":"Sensors"},{"issue":"22","key":"7255_CR34","doi-asserted-by":"publisher","first-page":"6600","DOI":"10.3390\/s20226600","volume":"20","author":"M Anagnostopoulos","year":"2020","unstructured":"Anagnostopoulos M et al (2020) Tracing your smart-home devices conversations: a real world iot traffic data-set. Sensors 20(22):6600","journal-title":"Sensors"},{"issue":"8","key":"7255_CR35","doi-asserted-by":"publisher","first-page":"5583","DOI":"10.1109\/TII.2020.3021689","volume":"17","author":"M Wo\u017aniak","year":"2020","unstructured":"Wo\u017aniak M, Si\u0142ka J, Wieczorek M, Alrashoud M (2020) Recurrent neural network model for iot and networking malware threat detection. IEEE Trans Ind Inform 17(8):5583\u20135594","journal-title":"IEEE Trans Ind Inform"},{"key":"7255_CR36","doi-asserted-by":"crossref","unstructured":"Ahmed AS, Kurnaz S, Khaleel AM (2023) Evaluation ddos attack detection through the application of machine learning techniques on the cicids2017 dataset in the field of information security. openaccess.altinbas.edu.tr","DOI":"10.18280\/mmep.100404"},{"key":"7255_CR37","doi-asserted-by":"crossref","unstructured":"Krsteski S, Tashkovska M, Sazdov B, Radojichikj L, Cholakoska A, Efnusheva D (2023) Intrusion detection with supervised and unsupervised learning using pycaret over cicids 2017 dataset. In: Computer Science On-line Conference, pp 125\u2013132. Springer","DOI":"10.1007\/978-3-031-35314-7_12"},{"issue":"2","key":"7255_CR38","doi-asserted-by":"publisher","first-page":"45","DOI":"10.29207\/joseit.v2i2.5411","volume":"2","author":"A Oyelakin","year":"2023","unstructured":"Oyelakin A, Ameen AO, Ogundele TS, Salau-Ibrahim T, Abdulrauf UT, Olufadi HI, Muhammad-Thani S (2023) Overview and exploratory analyses of cicids 2017 intrusion detection dataset. J Syst Eng Inf Technol (JOSEIT) 2(2):45\u201352","journal-title":"J Syst Eng Inf Technol (JOSEIT)"},{"key":"7255_CR39","unstructured":"Barkah AS, Selamat SR, Abidin ZZ, Wahyudi R (2023) Data generative model to detect the anomalies for ids imbalance cicids2017 dataset. TEM J 12(1)"},{"key":"7255_CR40","doi-asserted-by":"crossref","unstructured":"Azalmad M, El\u00a0Ayachi R, Biniz M (2023) Unveiling the performance insights: benchmarking anomaly-based intrusion detection systems using decision tree family algorithms on the cicids2017 dataset. In: International Conference on Business Intelligence, pp 202\u2013219. Springer","DOI":"10.1007\/978-3-031-37872-0_15"},{"issue":"1","key":"7255_CR41","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.11591\/ijece.v13i1.pp1134-1141","volume":"13","author":"J Jose","year":"2023","unstructured":"Jose J, Jose DV (2023) Deep learning algorithms for intrusion detection systems in internet of things using cic-ids 2017 dataset. Int J Electr Comput Eng (IJECE) 13(1):1134\u20131141","journal-title":"Int J Electr Comput Eng (IJECE)"},{"issue":"11","key":"7255_CR42","doi-asserted-by":"publisher","first-page":"180","DOI":"10.3390\/fi12110180","volume":"12","author":"A Mahfouz","year":"2020","unstructured":"Mahfouz A, Abuhussein A, Venugopal D, Shiva S (2020) Ensemble classifiers for network intrusion detection using a novel network attack dataset. Future Internet 12(11):180","journal-title":"Future Internet"},{"issue":"3","key":"7255_CR43","doi-asserted-by":"publisher","first-page":"1761","DOI":"10.1007\/s10586-020-03222-y","volume":"24","author":"S Krishnaveni","year":"2021","unstructured":"Krishnaveni S, Sivamohan S, Sridhar SS, Prabakaran S (2021) Efficient feature selection and classification through ensemble method for network intrusion detection on cloud computing. Clust Comput 24(3):1761\u20131779","journal-title":"Clust Comput"},{"issue":"4","key":"7255_CR44","doi-asserted-by":"publisher","first-page":"2339","DOI":"10.1007\/s10586-022-03735-8","volume":"26","author":"AK Mishra","year":"2023","unstructured":"Mishra AK, Paliwal S (2023) Mitigating cyber threats through integration of feature selection and stacking ensemble learning: the lgbm and random forest intrusion detection perspective. Clust Comput 26(4):2339\u20132350","journal-title":"Clust Comput"},{"key":"7255_CR45","doi-asserted-by":"publisher","first-page":"100206","DOI":"10.1016\/j.dajour.2023.100206","volume":"7","author":"AK Dey","year":"2023","unstructured":"Dey AK, Gupta GP, Sahu SP (2023) A metaheuristic-based ensemble feature selection framework for cyber threat detection in iot-enabled networks. Decis Anal J 7:100206","journal-title":"Decis Anal J"},{"key":"7255_CR46","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.comcom.2022.11.009","volume":"198","author":"AN Jahromi","year":"2023","unstructured":"Jahromi AN, Karimipour H, Dehghantanha A (2023) An ensemble deep federated learning cyber-threat hunting model for industrial internet of things. Comput Commun 198:108\u2013116","journal-title":"Comput Commun"},{"issue":"2","key":"7255_CR47","doi-asserted-by":"publisher","first-page":"7460","DOI":"10.1002\/cpe.7460","volume":"35","author":"AV Turukmane","year":"2023","unstructured":"Turukmane AV (2023) Forecasting the iot-based cyber threats using the hybrid forage dependent ensemble classifier. Concurr Comput: Pract Exp 35(2):7460","journal-title":"Concurr Comput: Pract Exp"},{"key":"7255_CR48","doi-asserted-by":"publisher","first-page":"8367","DOI":"10.1007\/s10586-024-04436-0","volume":"27","author":"A Nazir","year":"2024","unstructured":"Nazir A, He J, Zhu N, Anwar MS, Pathan MS (2024) Enhancing iot security: a collaborative framework integrating federated learning, dense neural networks, and blockchain. Clust Comput 27:8367\u20138392","journal-title":"Clust Comput"},{"key":"7255_CR49","doi-asserted-by":"publisher","first-page":"99837","DOI":"10.1109\/ACCESS.2022.3206425","volume":"10","author":"A Halbouni","year":"2022","unstructured":"Halbouni A, Gunawan TS, Habaebi MH, Halbouni M, Kartiwi M, Ahmad R (2022) Cnn-lstm: hybrid deep neural network for network intrusion detection system. IEEE Access 10:99837\u201399849","journal-title":"IEEE Access"},{"key":"7255_CR50","doi-asserted-by":"crossref","unstructured":"Nazir A, He J, Zhu N, Qureshi SS, Qureshi SU, Ullah F, Wajahat A, Pathan MS (2024) A deep learning-based novel hybrid cnn-lstm architecture for efficient detection of threats in the iot ecosystem. Ain Shams Eng J","DOI":"10.1016\/j.asej.2024.102777"},{"key":"7255_CR51","first-page":"101322","volume":"38","author":"HC Altunay","year":"2023","unstructured":"Altunay HC, Albayrak Z (2023) A hybrid cnn+ lstmbased intrusion detection system for industrial iot networks. Eng Sci Technol Int J 38:101322","journal-title":"Eng Sci Technol Int J"},{"issue":"5\u20136","key":"7255_CR52","doi-asserted-by":"publisher","first-page":"1973","DOI":"10.1007\/s00170-022-10329-6","volume":"123","author":"M Shahin","year":"2022","unstructured":"Shahin M, Chen FF, Hosseinzadeh A, Bouzary H, Rashidifar R (2022) A deep hybrid learning model for detection of cyber attacks in industrial iot devices. Int J Adv Manuf Technol 123(5\u20136):1973\u20131983","journal-title":"Int J Adv Manuf Technol"},{"key":"7255_CR53","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.future.2022.12.034","volume":"142","author":"MA Abdullah","year":"2023","unstructured":"Abdullah MA, Yu Y, Adu K, Imrana Y, Wang X, Cai J (2023) Hcl-classifier: Cnn and lstm based hybrid malware classifier for internet of things (iot). Future Gener Comput Syst 142:41\u201358","journal-title":"Future Gener Comput Syst"},{"issue":"1","key":"7255_CR54","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1186\/s13677-024-00722-9","volume":"13","author":"S Nandhini","year":"2024","unstructured":"Nandhini S, Rajeswari A, Shanker NR (2024) Cyber attack detection in iot-wsn devices with threat intelligence using hidden and connected layer based architectures. J Cloud Comput 13(1):159. https:\/\/doi.org\/10.1186\/s13677-024-00722-9","journal-title":"J Cloud Comput"},{"key":"7255_CR55","doi-asserted-by":"publisher","first-page":"21789","DOI":"10.1038\/s41598-024-72049-z","volume":"14","author":"A Qaddos","year":"2024","unstructured":"Qaddos A, Yaseen MU, Al-Shamayleh AS et al (2024) A novel intrusion detection framework for optimizing iot security. Sci Rep 14:21789. https:\/\/doi.org\/10.1038\/s41598-024-72049-z","journal-title":"Sci Rep"},{"key":"7255_CR56","doi-asserted-by":"publisher","first-page":"12077","DOI":"10.1038\/s41598-024-62861-y","volume":"14","author":"H El-Sofany","year":"2024","unstructured":"El-Sofany H, El-Seoud SA, Karam OH et al (2024) Using machine learning algorithms to enhance iot system security. Sci Rep 14:12077. https:\/\/doi.org\/10.1038\/s41598-024-62861-y","journal-title":"Sci Rep"},{"key":"7255_CR57","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/s10586-024-04904-7","volume":"28","author":"K Kharoubi","year":"2025","unstructured":"Kharoubi K, Cherbal S, Mechta D et al (2025) Network intrusion detection system using convolutional neural networks: Nids-dl-cnn for iot security. Clust Comput 28:219. https:\/\/doi.org\/10.1007\/s10586-024-04904-7","journal-title":"Clust Comput"},{"key":"7255_CR58","doi-asserted-by":"publisher","first-page":"13809","DOI":"10.1007\/s10586-024-04638-6","volume":"27","author":"A Sharma","year":"2024","unstructured":"Sharma A, Bhushan K (2024) A hybrid approach based on puf and ml to protect mqtt based iot system from ddos attacks. Clust Comput 27:13809\u201313834. https:\/\/doi.org\/10.1007\/s10586-024-04638-6","journal-title":"Clust Comput"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07255-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07255-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07255-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T18:11:06Z","timestamp":1745431866000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07255-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,23]]},"references-count":58,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["7255"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07255-1","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,23]]},"assertion":[{"value":"26 March 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"775"}}