{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T04:41:01Z","timestamp":1777956061414,"version":"3.51.4"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T00:00:00Z","timestamp":1751068800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T00:00:00Z","timestamp":1751068800000},"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":["Evolving Systems"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s12530-025-09706-9","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T23:57:58Z","timestamp":1751068678000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Evolving security measures for IoT medical data in cloud environments"],"prefix":"10.1007","volume":"16","author":[{"given":"C.","family":"Dhaya","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G.","family":"Niranjana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B.","family":"Prakash","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,28]]},"reference":[{"issue":"11","key":"9706_CR1","doi-asserted-by":"publisher","first-page":"3519","DOI":"10.3390\/s24113519","volume":"24","author":"M Alalhareth","year":"2024","unstructured":"Alalhareth M, Hong SC (2024) Enhancing the internet of medical things (IoMT) security with meta-learning: a performance-driven approach for ensemble intrusion detection systems. Sensors 24(11):3519","journal-title":"Sensors"},{"key":"9706_CR2","doi-asserted-by":"crossref","unstructured":"Areia J, Bispo I, Santos L, Costa RLDC (2024) IoMT-TrafficData: Dataset and tools for benchmarking intrusion detection in internet of medical things. IEEE Access.","DOI":"10.1109\/ACCESS.2024.3437214"},{"key":"9706_CR3","doi-asserted-by":"crossref","unstructured":"Arunkumar R, Navanitha S, Padmavathi B, Snekaa V (2024) Hybrid SVM approach for enhanced DDoS attack detection using machine learning in cloud environment. In 2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA) IEEE 1\u20134.","DOI":"10.1109\/AIMLA59606.2024.10531330"},{"key":"9706_CR4","doi-asserted-by":"crossref","unstructured":"Bella HK, Vasundra S (2024) Healthcare intrusion detection using hybrid correlation-based feature selection-bat optimization algorithm with convolutional neural network: a hybrid correlation-based feature selection for intrusion detection systems. Int J Advanced Computer Sci Appl 15(1).","DOI":"10.14569\/IJACSA.2024.0150166"},{"key":"9706_CR5","doi-asserted-by":"crossref","unstructured":"Berguiga A, Harchay A, Massaoudi A (2025) HIDS-IoMT: A deep Learning-Based intelligent intrusion detection system for the internet of medical things. IEEE Access.","DOI":"10.1109\/ACCESS.2025.3543127"},{"issue":"1","key":"9706_CR6","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s13677-025-00733-0","volume":"14","author":"B Bhasker","year":"2025","unstructured":"Bhasker B, Kaliraj S, Gobinath C, Sivakumar V (2025) Optimizing energy task offloading technique using IoMT cloud in healthcare applications. J Cloud Comput 14(1):9","journal-title":"J Cloud Comput"},{"issue":"1","key":"9706_CR7","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1007\/s10665-023-10309-z","volume":"144","author":"PB Dash","year":"2024","unstructured":"Dash PB, Senapati MR, Behera HS, Nayak J, Vimal S (2024) Self-adaptive memetic firefly algorithm and CatBoost-based security framework for IoT healthcare environment. J Eng Math 144(1):6","journal-title":"J Eng Math"},{"issue":"1","key":"9706_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12652-022-03832-x","volume":"15","author":"W El-Shafai","year":"2024","unstructured":"El-Shafai W, Khallaf F, El-Rabaie ESM, El-Samie FEA (2024) Proposed 3D chaos-based medical image cryptosystem for secure cloud-IoMT eHealth communication services. J Ambient Intell Humaniz Comput 15(1):1\u201328","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"17","key":"9706_CR9","doi-asserted-by":"publisher","first-page":"3541","DOI":"10.3390\/electronics12173541","volume":"12","author":"N Faruqui","year":"2023","unstructured":"Faruqui N, Yousuf MA, Whaiduzzaman M, Azad AKM, Alyami SA, Li\u00f2 P, Kabir MA, Moni MA (2023) SafetyMed: A novel IoMT intrusion detection system using CNN-LSTM hybridization. Electronics 12(17):3541","journal-title":"Electronics"},{"key":"9706_CR10","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.comcom.2024.02.023","volume":"218","author":"I Ioannou","year":"2024","unstructured":"Ioannou I, Nagaradjane P, Angin P, Balasubramanian P, Kavitha KJ, Murugan P, Vassiliou V (2024) GEMLIDS-MIOT: A green effective machine learning intrusion detection system based on federated learning for medical IoT network security hardening. Comput Commun 218:209\u2013239","journal-title":"Comput Commun"},{"key":"9706_CR11","doi-asserted-by":"crossref","unstructured":"Kabir MR, Ray S (2024) DT-IoMT: A digital twin reference model for secure internet of medical things. In 2024 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), IEEE. pp. 433\u2013438.","DOI":"10.1109\/ISVLSI61997.2024.00084"},{"key":"9706_CR12","doi-asserted-by":"crossref","unstructured":"Kalakoti R, N\u00f5mm S, Bahsi H (2024) Explainable transformer-based intrusion detection in internet of medical things (IoMT) Networks. In 2024 International Conference on Machine Learning and Applications (ICMLA), IEEE. pp. 1164\u20131169.","DOI":"10.1109\/ICMLA61862.2024.00179"},{"issue":"28","key":"9706_CR13","doi-asserted-by":"publisher","first-page":"70793","DOI":"10.1007\/s11042-024-18331-8","volume":"83","author":"Y Mohan","year":"2024","unstructured":"Mohan Y, Yadav RK, Manjul M (2024) Seagull optimization algorithm for node localization in wireless sensor networks. Multimedia Tools Appl 83(28):70793\u201370814","journal-title":"Multimedia Tools Appl"},{"issue":"10","key":"9706_CR14","doi-asserted-by":"publisher","first-page":"3223","DOI":"10.3390\/s24103223","volume":"24","author":"Pritika","year":"2024","unstructured":"Pritika, Shanmugam B, Azam S (2024) Risk evaluation and attack detection in heterogeneous IoMT devices using hybrid fuzzy logic analytical approach. Sensors 24(10):3223","journal-title":"Sensors"},{"issue":"10","key":"9706_CR15","doi-asserted-by":"publisher","first-page":"6001","DOI":"10.1007\/s10115-024-02149-9","volume":"66","author":"PG Shambharkar","year":"2024","unstructured":"Shambharkar PG, Sharma N (2024) Deep learning-empowered intrusion detection framework for the Internet of Medical Things environment. Knowl Inf Syst 66(10):6001\u20136050","journal-title":"Knowl Inf Syst"},{"key":"9706_CR16","doi-asserted-by":"publisher","first-page":"17328","DOI":"10.1109\/ACCESS.2024.3354034","volume":"12","author":"G Sripriyanka","year":"2024","unstructured":"Sripriyanka G, Mahendran A (2024) Securing IoMT: A hybrid model for DDoS attack detection and COVID-19 classification. IEEE Access 12:17328\u201317348","journal-title":"IEEE Access"},{"key":"9706_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.fraope.2023.100056","volume":"6","author":"Z Sun","year":"2024","unstructured":"Sun Z, An G, Yang Y, Liu Y (2024) Optimized machine learning enabled intrusion detection 2 system for internet of medical things. Franklin Open 6:100056","journal-title":"Franklin Open"},{"issue":"4","key":"9706_CR18","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1007\/s10586-024-04874-w","volume":"28","author":"Surbhi","year":"2025","unstructured":"Surbhi, Chauhan NR, Dahiya N (2025) Optimizing XGBoost hyperparameters using the dragonfly algorithm for enhanced cyber attack detection in the internet of healthcare things (IoHT). Clust Comput 28(4):230","journal-title":"Clust Comput"},{"key":"9706_CR19","doi-asserted-by":"crossref","unstructured":"Suresh S, Srivatsala V, Kanagalakshmi TK, Suneetha V, Hegde G, Hithesh R (2024) Application of Body Sensor Networks in Health Care Using the Internet of Medical Things (IoMT). In The Next Generation Innovation in IoT and Cloud Computing with Applications CRC Press. pp. 131\u2013156.","DOI":"10.1201\/9781003406723-8"},{"key":"9706_CR20","doi-asserted-by":"crossref","unstructured":"Yadav R, Pradeepa P, Srinivasan S, Rajora CS, Rajalakshmi R (2024) A novel healthcare framework for ambient assisted living using the internet of medical things (IOMT) and deep neural network. Measurement: Sensors 33:101111.","DOI":"10.1016\/j.measen.2024.101111"},{"key":"9706_CR21","doi-asserted-by":"crossref","unstructured":"Zukaib U, Cui X, Zheng C, Hassan M, Shen Z (2024) Meta-IDS: meta-learning based smart intrusion detection system for internet of medical things (IoMT) network. IEEE Internet Things J 11(13): 23080-23095","DOI":"10.1109\/JIOT.2024.3387294"}],"container-title":["Evolving Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-025-09706-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12530-025-09706-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-025-09706-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T09:53:54Z","timestamp":1760781234000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12530-025-09706-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,28]]},"references-count":21,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["9706"],"URL":"https:\/\/doi.org\/10.1007\/s12530-025-09706-9","relation":{},"ISSN":["1868-6478","1868-6486"],"issn-type":[{"value":"1868-6478","type":"print"},{"value":"1868-6486","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,28]]},"assertion":[{"value":"25 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 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":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human or animal subjects performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal rights"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"80"}}