{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T03:13:23Z","timestamp":1772594003430,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T00:00:00Z","timestamp":1742601600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T00:00:00Z","timestamp":1742601600000},"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":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s13042-025-02599-9","type":"journal-article","created":{"date-parts":[[2025,3,23]],"date-time":"2025-03-23T00:12:28Z","timestamp":1742688748000},"page":"5821-5839","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Privacy-preserving intrusion detection with federated learning: enhancing privacy and efficiency in IoT networks"],"prefix":"10.1007","volume":"16","author":[{"given":"Hemant","family":"Sharma","sequence":"first","affiliation":[]},{"given":"Gyan Singh","family":"Yadav","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,22]]},"reference":[{"issue":"6","key":"2599_CR1","first-page":"1937","volume":"22","author":"N Hamdi","year":"2023","unstructured":"Hamdi N (2023) Federated learning-based intrusion detection system for internet of things. Int J Inform Secur 22(6):1937\u20131948","journal-title":"Int J Inform Secur"},{"key":"2599_CR2","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.future.2023.10.005","volume":"153","author":"TT Thein","year":"2024","unstructured":"Thein TT, Shiraishi Y, Morii M (2024) Personalized federated learning-based intrusion detection system: Poisoning attack and defense. Future Generation Computer Syst 153:182\u2013192","journal-title":"Future Generation Computer Syst"},{"issue":"1","key":"2599_CR3","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1007\/s12083-021-01275-3","volume":"15","author":"AO Bang","year":"2022","unstructured":"Bang AO, Rao UP (2022) Embof-rpl: Improved rpl for early detection and isolation of rank attack in rpl-based internet of things. Peer-to-Peer Netw Appl 15(1):642\u2013665","journal-title":"Peer-to-Peer Netw Appl"},{"key":"2599_CR4","doi-asserted-by":"crossref","unstructured":"Sharma S (2021) Aiemla: artificial intelligence enabled machine learning approach for routing attacks on internet of things","DOI":"10.1007\/s11227-021-03833-1"},{"key":"2599_CR5","doi-asserted-by":"crossref","unstructured":"Sharma M, Elmiligi H, Gebali F, Verma A (2019) Simulating attacks for rpl and generating multi-class dataset for supervised machine learning. In: 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 0020\u20130026. IEEE","DOI":"10.1109\/IEMCON.2019.8936142"},{"issue":"3","key":"2599_CR6","doi-asserted-by":"publisher","first-page":"2309","DOI":"10.1109\/TNSM.2022.3177512","volume":"19","author":"L Lavaur","year":"2022","unstructured":"Lavaur L, Pahl M-O, Busnel Y, Autrel F (2022) The evolution of federated learning-based intrusion detection and mitigation: a survey. IEEE Trans Netw Service Manag 19(3):2309\u20132332","journal-title":"IEEE Trans Netw Service Manag"},{"issue":"2","key":"2599_CR7","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1109\/TII.2021.3126728","volume":"19","author":"P Ruzafa-Alc\u00e1zar","year":"2021","unstructured":"Ruzafa-Alc\u00e1zar P, Fern\u00e1ndez-Saura P, M\u00e1rmol-Campos E, Gonz\u00e1lez-Vidal A, Hern\u00e1ndez-Ramos JL, Bernal-Bernabe J, Skarmeta AF (2021) Intrusion detection based on privacy-preserving federated learning for the industrial iot. IEEE Trans Ind Inform 19(2):1145\u20131154","journal-title":"IEEE Trans Ind Inform"},{"key":"2599_CR8","unstructured":"McMahan B, Moore E, Ramage D, Hampson S, Arcas BA (2017) Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282. PMLR"},{"key":"2599_CR9","doi-asserted-by":"crossref","unstructured":"Nguyen TD, Rieger P, Miettinen M, Sadeghi A-R (2020) Poisoning attacks on federated learning-based iot intrusion detection system. In: Proc. Workshop Decentralized IoT Syst. Secur.(DISS), vol. 79","DOI":"10.14722\/diss.2020.23003"},{"key":"2599_CR10","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.jnca.2019.06.019","volume":"143","author":"S Rathore","year":"2019","unstructured":"Rathore S, Kwon BW, Park JH (2019) Blockseciotnet: Blockchain-based decentralized security architecture for iot network. J Netw Comput Appl 143:167\u2013177","journal-title":"J Netw Comput Appl"},{"issue":"2","key":"2599_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3298981","volume":"10","author":"Q Yang","year":"2019","unstructured":"Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: Concept and applications. ACM Trans Intell Syst Technol (TIST) 10(2):1\u201319","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"2599_CR12","doi-asserted-by":"crossref","unstructured":"Kairouz P, McMahan HB, Avent B, Bellet A, Bennis M, Bhagoji AN, Bonawitz K, Charles Z, Cormode G, Cummings R et\u00a0al. (2021) Advances and open problems in federated learning. Foundations and trends\u00ae in machine learning 14(1\u20132):1\u2013210","DOI":"10.1561\/2200000083"},{"key":"2599_CR13","doi-asserted-by":"crossref","unstructured":"Nguyen TD, Marchal S, Miettinen M, Fereidooni H, Asokan N, Sadeghi A-R (2019) D\u00efot: A federated self-learning anomaly detection system for iot. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 756\u2013767. IEEE","DOI":"10.1109\/ICDCS.2019.00080"},{"key":"2599_CR14","doi-asserted-by":"publisher","first-page":"217463","DOI":"10.1109\/ACCESS.2020.3041793","volume":"8","author":"Z Chen","year":"2020","unstructured":"Chen Z, Lv N, Liu P, Fang Y, Chen K, Pan W (2020) Intrusion detection for wireless edge networks based on federated learning. IEEE Access 8:217463\u2013217472","journal-title":"IEEE Access"},{"key":"2599_CR15","doi-asserted-by":"crossref","unstructured":"Cetin B, Lazar A, Kim J, Sim A, Wu K (2019) Federated wireless network intrusion detection. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 6004\u20136006. IEEE","DOI":"10.1109\/BigData47090.2019.9005507"},{"issue":"8","key":"2599_CR16","doi-asserted-by":"publisher","first-page":"5615","DOI":"10.1109\/TII.2020.3023430","volume":"17","author":"B Li","year":"2020","unstructured":"Li B, Wu Y, Song J, Lu R, Li T, Zhao L (2020) Deepfed: Federated deep learning for intrusion detection in industrial cyber-physical systems. IEEE Trans Ind Inform 17(8):5615\u20135624","journal-title":"IEEE Trans Ind Inform"},{"key":"2599_CR17","doi-asserted-by":"crossref","unstructured":"Fan Y, Li Y, Zhan M, Cui H, Zhang Y (2020) Iotdefender: A federated transfer learning intrusion detection framework for 5g iot. In: 2020 IEEE 14th International Conference on Big Data Science and Engineering (BigDataSE), pp. 88\u201395. IEEE","DOI":"10.1109\/BigDataSE50710.2020.00020"},{"key":"2599_CR18","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1109\/OJCOMS.2020.3044323","volume":"2","author":"Y Sun","year":"2020","unstructured":"Sun Y, Esaki H, Ochiai H (2020) Adaptive intrusion detection in the networking of large-scale lans with segmented federated learning. IEEE Open J Commun Soc 2:102\u2013112","journal-title":"IEEE Open J Commun Soc"},{"issue":"5","key":"2599_CR19","doi-asserted-by":"publisher","first-page":"3930","DOI":"10.1109\/JIOT.2021.3100755","volume":"9","author":"SI Popoola","year":"2021","unstructured":"Popoola SI, Ande R, Adebisi B, Gui G, Hammoudeh M, Jogunola O (2021) Federated deep learning for zero-day botnet attack detection in iot-edge devices. IEEE Internet Things J 9(5):3930\u20133944","journal-title":"IEEE Internet Things J"},{"key":"2599_CR20","doi-asserted-by":"crossref","unstructured":"Issa MA, Ibnkahla M, Matrawy A, Eldosouky A (2024) Temporal partitioned federated learning for iot intrusion detection systems. In: 2024 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1\u20136. IEEE","DOI":"10.1109\/WCNC57260.2024.10570551"},{"key":"2599_CR21","doi-asserted-by":"crossref","unstructured":"Bhavsar M, Bekele Y, Roy K, Kelly J, Limbrick D (2024) Fl-ids: Federated learning-based intrusion detection system using edge devices for transportation iot. IEEE Access","DOI":"10.1109\/ACCESS.2024.3386631"},{"key":"2599_CR22","doi-asserted-by":"crossref","unstructured":"Radjaa B, Nabila L, Salameh HB (2023) Federated deep learning-based intrusion detection approach for enhancing privacy in fog-iot networks. In: 2023 10th International Conference on Internet of Things: Systems, Management and Security (IOTSMS), pp. 156\u2013160. IEEE","DOI":"10.1109\/IOTSMS59855.2023.10325826"},{"key":"2599_CR23","doi-asserted-by":"crossref","unstructured":"Nuaimi M, Fourati LC, Hamed BB (2023) A scalable intrusion detection approach for industrial internet of things based on federated learning and attention mechanism. In: 2023 IEEE Symposium on Computers and Communications (ISCC), pp. 1\u20134. IEEE","DOI":"10.1109\/ISCC58397.2023.10218054"},{"key":"2599_CR24","doi-asserted-by":"crossref","unstructured":"Naeem F, Malik AW, Khan SA, Jabeen F (2023) Enhancing intrusion detection: Leveraging federated learning and hybrid machine learning algorithms on ton_iot dataset. In: 2023 International Conference on Frontiers of Information Technology (FIT), pp. 73\u201378. IEEE","DOI":"10.1109\/FIT60620.2023.00023"},{"key":"2599_CR25","doi-asserted-by":"crossref","unstructured":"Almesleh Z, Gouissem A, Hamila R (2024) Federated learning with kalman filter for intrusion detection in iot environment. In: 2024 IEEE 8th Energy Conference (ENERGYCON), pp. 1\u20136. IEEE","DOI":"10.1109\/ENERGYCON58629.2024.10488796"},{"issue":"7","key":"2599_CR26","doi-asserted-by":"publisher","first-page":"8159","DOI":"10.1109\/TII.2022.3216575","volume":"19","author":"J Zhang","year":"2022","unstructured":"Zhang J, Luo C, Carpenter M, Min G (2022) Federated learning for distributed iiot intrusion detection using transfer approaches. IEEE Trans Ind Inform 19(7):8159\u20138169","journal-title":"IEEE Trans Ind Inform"},{"key":"2599_CR27","doi-asserted-by":"crossref","unstructured":"Rajesh LT, Das T, Shukla RM, Sengupta S (2023) Give and take: Federated transfer learning for industrial iot network intrusion detection. In: 2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp. 2365\u20132371. IEEE","DOI":"10.1109\/TrustCom60117.2023.00333"},{"key":"2599_CR28","doi-asserted-by":"crossref","unstructured":"Elnakib O, Shaaban E, Mahmoud M, Emara K (2023) Evaluation of centralized, distributed and federated learning for iot intrusion detection systems. In: 2023 Eleventh International Conference on Intelligent Computing and Information Systems (ICICIS), pp. 315\u2013320. IEEE","DOI":"10.1109\/ICICIS58388.2023.10391151"},{"issue":"5","key":"2599_CR29","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/MWC.2007.4396943","volume":"14","author":"B Sun","year":"2007","unstructured":"Sun B, Osborne L, Xiao Y, Guizani S (2007) Intrusion detection techniques in mobile ad hoc and wireless sensor networks. IEEE Wirel Commun 14(5):56\u201363","journal-title":"IEEE Wirel Commun"},{"key":"2599_CR30","doi-asserted-by":"crossref","unstructured":"Arrington B, Barnett L, Rufus R, Esterline A (2016) Behavioral modeling intrusion detection system (bmids) using internet of things (iot) behavior-based anomaly detection via immunity-inspired algorithms. In: 2016 25th International Conference on Computer Communication and Networks (ICCCN), pp. 1\u20136. IEEE","DOI":"10.1109\/ICCCN.2016.7568495"},{"issue":"4","key":"2599_CR31","doi-asserted-by":"publisher","first-page":"3496","DOI":"10.1109\/COMST.2018.2844742","volume":"20","author":"E Benkhelifa","year":"2018","unstructured":"Benkhelifa E, Welsh T, Hamouda W (2018) A critical review of practices and challenges in intrusion detection systems for iot: Toward universal and resilient systems. IEEE Commun Surveys Tutorials 20(4):3496\u20133509","journal-title":"IEEE Commun Surveys Tutorials"},{"key":"2599_CR32","doi-asserted-by":"crossref","unstructured":"Poston HE (2012) A brief taxonomy of intrusion detection strategies. In: 2012 IEEE National Aerospace and Electronics Conference (NAECON), pp. 255\u2013263. IEEE","DOI":"10.1109\/NAECON.2012.6531064"},{"key":"2599_CR33","doi-asserted-by":"crossref","unstructured":"Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the kdd cup 99 data set. In: 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1\u20136. Ieee","DOI":"10.1109\/CISDA.2009.5356528"},{"issue":"1","key":"2599_CR34","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1109\/SURV.2013.050113.00191","volume":"16","author":"I Butun","year":"2013","unstructured":"Butun I, Morgera SD, Sankar R (2013) A survey of intrusion detection systems in wireless sensor networks. IEEE Commun Surveys Tutorials 16(1):266\u2013282","journal-title":"IEEE Commun Surveys Tutorials"},{"key":"2599_CR35","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.jnca.2017.02.009","volume":"84","author":"BB Zarpel\u00e3o","year":"2017","unstructured":"Zarpel\u00e3o BB, Miani RS, Kawakani CT, De Alvarenga SC (2017) A survey of intrusion detection in internet of things. J Netw Comput Appl 84:25\u201337","journal-title":"J Netw Comput Appl"},{"key":"2599_CR36","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et\u00a0al. (2011) Scikit-learn: Machine learning in python. the Journal of machine Learning research 12:2825\u20132830"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02599-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02599-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02599-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T11:00:10Z","timestamp":1757156410000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02599-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,22]]},"references-count":36,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["2599"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02599-9","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,22]]},"assertion":[{"value":"19 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 March 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":"This manuscript possesses no funding and Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not required for this study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"I give my consent to publish this manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"No specific material has been used in this study.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Materials availability"}}]}}