{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T12:14:10Z","timestamp":1778328850027,"version":"3.51.4"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s00521-026-11890-x","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T03:00:17Z","timestamp":1776049217000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DP-DL-ZT: a zero-trust-enhanced differential privacy framework with CNN-LSTM for cyber threat detection in IoT healthcare"],"prefix":"10.1007","volume":"38","author":[{"given":"Ibrahim M.","family":"Hezam","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahmoud M.","family":"Ismail","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2737-5649","authenticated-orcid":false,"given":"Ahmed M.","family":"Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karam","family":"Sallam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed","family":"Abdel-Basset","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,13]]},"reference":[{"key":"11890_CR1","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/j.comnet.2018.11.025","volume":"148","author":"WH Hassan","year":"2019","unstructured":"Hassan WH (2019) Current research on Internet of Things (IoT) security: a survey. Comput Networks 148:283\u2013294","journal-title":"Comput Networks"},{"key":"11890_CR2","doi-asserted-by":"crossref","unstructured":"Mahmoud R, Yousuf T, Aloul F, Zualkernan I (2015) Internet of things (IoT) security: current status, challenges and prospective measures. In 2015 10th international conference for internet technology and secured transactions (ICITST), IEEE, pp 336\u2013341","DOI":"10.1109\/ICITST.2015.7412116"},{"issue":"3","key":"11890_CR3","doi-asserted-by":"publisher","first-page":"1646","DOI":"10.1109\/COMST.2020.2988293","volume":"22","author":"MA Al-Garadi","year":"2020","unstructured":"Al-Garadi MA, Mohamed A, Al-Ali AK, Du X, Ali I, Guizani M (2020) A survey of machine and deep learning methods for internet of things (IoT) security. IEEE Commun Surv Tutor 22(3):1646\u20131685","journal-title":"IEEE Commun Surv Tutor"},{"issue":"13","key":"11890_CR4","doi-asserted-by":"publisher","first-page":"10474","DOI":"10.1109\/JIOT.2021.3062630","volume":"8","author":"MN Bhuiyan","year":"2021","unstructured":"Bhuiyan MN, Rahman MM, Billah MM, Saha D (2021) Internet of things (IoT): a review of its enabling technologies in healthcare applications, standards protocols, security, and market opportunities. IEEE Internet Things J 8(13):10474\u201310498","journal-title":"IEEE Internet Things J"},{"key":"11890_CR5","doi-asserted-by":"publisher","first-page":"678","DOI":"10.1109\/ACCESS.2015.2437951","volume":"3","author":"SMR Islam","year":"2015","unstructured":"Islam SMR, Kwak D, Kabir MDH, Hossain M, Kwak K-S (2015) The internet of things for health care: a comprehensive survey. IEEE Access 3:678\u2013708","journal-title":"IEEE Access"},{"key":"11890_CR6","doi-asserted-by":"publisher","first-page":"3660","DOI":"10.1109\/ACCESS.2020.3047960","volume":"9","author":"F Alshehri","year":"2020","unstructured":"Alshehri F, Muhammad G (2020) A comprehensive survey of the Internet of Things (IoT) and AI-based smart healthcare. IEEE Access 9:3660\u20133678","journal-title":"IEEE Access"},{"issue":"4","key":"11890_CR7","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1016\/j.dcan.2020.12.002","volume":"7","author":"TJ Saleem","year":"2021","unstructured":"Saleem TJ, Chishti MA (2021) Deep learning for the internet of things: potential benefits and use-cases. Digit Commun Networks 7(4):526\u2013542","journal-title":"Digit Commun Networks"},{"key":"11890_CR8","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.iotcps.2023.09.003","volume":"4","author":"A Aldhaheri","year":"2023","unstructured":"Aldhaheri A, Alwahedi F, Ferrag MA, Battah A (2023) Deep learning for cyber threat detection in IoT networks: a review. Internet things cyber-physical syst 4:110\u2013128","journal-title":"Internet things cyber-physical syst"},{"key":"11890_CR9","doi-asserted-by":"crossref","unstructured":"Jha RS, Ojha K, Mishra A, Mishra R, Kaushik A (2024) Cyber-Attacks and Anomaly detection on CICIDS-2017 dataset using ER-VEC, In 2024 2nd international conference on disruptive technologies (ICDT), IEEE, pp 1453\u20131458","DOI":"10.1109\/ICDT61202.2024.10489209"},{"issue":"02","key":"11890_CR10","doi-asserted-by":"publisher","first-page":"254","DOI":"10.47392\/IRJAEH.2024.0041","volume":"2","author":"ZI Khan","year":"2024","unstructured":"Khan ZI, Afzal MM, Shamsi KN (2024) A comprehensive study on CIC-IDS2017 dataset for intrusion detection systems. Int Res J Adv Eng Hub 2(02):254\u2013260","journal-title":"Int Res J Adv Eng Hub"},{"key":"11890_CR11","doi-asserted-by":"crossref","unstructured":"Selvam R, Velliangiri S (2024) An improving intrusion detection model based on novel CNN Technique using recent CIC-IDS datasets, In 2024 International conference on distributed computing and optimization techniques (ICDCOT), IEEE, pp 1\u20136","DOI":"10.1109\/ICDCOT61034.2024.10515433"},{"key":"11890_CR12","doi-asserted-by":"publisher","first-page":"6476274","DOI":"10.1155\/2022\/6476274","volume":"2022","author":"Y He","year":"2022","unstructured":"He Y, Huang D, Chen L, Ni Y, Ma X (2022) A survey on zero trust architecture: Challenges and future trends. Wirel Commun Mob Comput 2022:6476274","journal-title":"Wirel Commun Mob Comput"},{"key":"11890_CR13","doi-asserted-by":"publisher","first-page":"102436","DOI":"10.1016\/j.cose.2021.102436","volume":"110","author":"C Buck","year":"2021","unstructured":"Buck C, Olenberger C, Schweizer A, V\u00f6lter F, Eymann T (2021) Never trust, always verify: a multivocal literature review on current knowledge and research gaps of zero-trust. Comput Secur 110:102436","journal-title":"Comput Secur"},{"key":"11890_CR14","first-page":"207","volume":"800","author":"VA Stafford","year":"2020","unstructured":"Stafford VA (2020) Zero trust architecture. NIST Spec Publ 800:207","journal-title":"NIST Spec Publ"},{"key":"11890_CR15","doi-asserted-by":"publisher","first-page":"1653","DOI":"10.1007\/s10796-021-10199-5","volume":"26","author":"S Li","year":"2022","unstructured":"Li S, Iqbal M, Saxena N (2022) Future industry internet of things with zero-trust security. Inf Syst Front 26:1653\u20131666","journal-title":"Inf Syst Front"},{"key":"11890_CR16","doi-asserted-by":"publisher","first-page":"103523","DOI":"10.1016\/j.adhoc.2024.103523","volume":"161","author":"F Nawshin","year":"2024","unstructured":"Nawshin F, Unal D, Hammoudeh M, Suganthan PN (2024) AI-powered malware detection with differential privacy for zero trust security in internet of things networks. Ad Hoc Netw 161:103523","journal-title":"Ad Hoc Netw"},{"key":"11890_CR17","doi-asserted-by":"crossref","unstructured":"Abadi M et al. (2016) Deep learning with differential privacy, In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, pp 308\u2013318","DOI":"10.1145\/2976749.2978318"},{"key":"11890_CR18","doi-asserted-by":"publisher","first-page":"48901","DOI":"10.1109\/ACCESS.2019.2909559","volume":"7","author":"J Zhao","year":"2019","unstructured":"Zhao J, Chen Y, Zhang W (2019) Differential privacy preservation in deep learning: challenges, opportunities and solutions. IEEE Access 7:48901\u201348911","journal-title":"IEEE Access"},{"key":"11890_CR19","doi-asserted-by":"publisher","first-page":"103161","DOI":"10.1016\/j.adhoc.2023.103161","volume":"145","author":"W Huang","year":"2023","unstructured":"Huang W, Xie X, Wang Z, Feng J, Han G, Zhang W (2023) ZT-access: a combining zero trust access control with attribute-based encryption scheme against compromised devices in power IoT environments. Ad Hoc Netw 145:103161","journal-title":"Ad Hoc Netw"},{"key":"11890_CR20","doi-asserted-by":"publisher","first-page":"103414","DOI":"10.1016\/j.adhoc.2024.103414","volume":"156","author":"C Zanasi","year":"2024","unstructured":"Zanasi C, Russo S, Colajanni M (2024) Flexible zero trust architecture for the cybersecurity of industrial IoT infrastructures. Ad Hoc Netw 156:103414","journal-title":"Ad Hoc Netw"},{"key":"11890_CR21","doi-asserted-by":"crossref","unstructured":"Munir MS, Proddatoori S, Muralidhara M, Saad W, Han Z, Shetty S. (2024) A zero trust framework for realization and defense against generative AI attacks in power grid, arXiv Prepr. http:\/\/arxiv.org\/abs\/2403.06388","DOI":"10.1109\/ICC51166.2024.10622574"},{"issue":"13","key":"11890_CR22","doi-asserted-by":"publisher","first-page":"10248","DOI":"10.1109\/JIOT.2020.3041042","volume":"8","author":"B Chen","year":"2020","unstructured":"Chen B et al (2020) A security awareness and protection system for 5G smart healthcare based on zero-trust architecture. IEEE Internet Things J 8(13):10248\u201310263","journal-title":"IEEE Internet Things J"},{"key":"11890_CR23","doi-asserted-by":"crossref","unstructured":"Samaniego M, Deters R (2018) Zero-trust hierarchical management in IoT, In 2018 IEEE international congress on internet of things (ICIOT), IEEE, pp 88\u201395","DOI":"10.1109\/ICIOT.2018.00019"},{"issue":"2","key":"11890_CR24","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MWC.001.2300405","volume":"31","author":"M Hussain","year":"2024","unstructured":"Hussain M, Pal S, Jadidi Z, Foo E, Kanhere S (2024) Federated zero trust architecture using artificial intelligence. IEEE Wirel Commun 31(2):30\u201335","journal-title":"IEEE Wirel Commun"},{"key":"11890_CR25","doi-asserted-by":"publisher","DOI":"10.1109\/access.2024.3402341","author":"B Zyoud","year":"2024","unstructured":"Zyoud B, Lutfi SL (2024) The role of information security culture in zero trust adoption: insights from UAE organizations. IEEE Access. https:\/\/doi.org\/10.1109\/access.2024.3402341","journal-title":"IEEE Access"},{"issue":"2","key":"11890_CR26","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MWC.001.2300375","volume":"31","author":"Y Liu","year":"2024","unstructured":"Liu Y, Su Z, Peng H, Xiang Y, Wang W, Li R (2024) Zero trust-based mobile network security architecture. IEEE Wirel Commun 31(2):82\u201388","journal-title":"IEEE Wirel Commun"},{"issue":"2","key":"11890_CR27","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/MWC.001.2300355","volume":"31","author":"M Asad","year":"2024","unstructured":"Asad M, Otoum S (2024) Integrative federated learning and zero-trust approach for secure wireless communications. IEEE Wirel Commun 31(2):14\u201320","journal-title":"IEEE Wirel Commun"},{"key":"11890_CR28","doi-asserted-by":"publisher","first-page":"101176","DOI":"10.1016\/j.measen.2024.101176","volume":"33","author":"G Ganapathy","year":"2024","unstructured":"Ganapathy G, Anand SJ, Jayaprakash M, Lakshmi S, Banu Priya V (2024) Networks, a blockchain based federated deep learning model for secured data transmission in healthcare IOT. Meas Sens 33:101176","journal-title":"Meas Sens"},{"key":"11890_CR29","doi-asserted-by":"publisher","first-page":"103560","DOI":"10.1016\/j.cose.2023.103560","volume":"136","author":"UK Lilhore","year":"2024","unstructured":"Lilhore UK, Dalal S, Simaiya S (2024) A cognitive security framework for detecting intrusions in IoT and 5G utilizing deep learning. Comput Secur 136:103560","journal-title":"Comput Secur"},{"key":"11890_CR30","doi-asserted-by":"publisher","first-page":"105511","DOI":"10.1016\/j.bspc.2023.105511","volume":"88","author":"AS Nadhan","year":"2024","unstructured":"Nadhan AS, Jacob IJ (2024) Enhancing healthcare security in the digital era: safeguarding medical images with lightweight cryptographic techniques in IoT healthcare applications. Biomed Signal Process Control 88:105511","journal-title":"Biomed Signal Process Control"},{"key":"11890_CR31","doi-asserted-by":"publisher","first-page":"120209","DOI":"10.1016\/j.ins.2024.120209","volume":"662","author":"AD Aguru","year":"2024","unstructured":"Aguru AD, Erukala SB (2024) A lightweight multi-vector DDoS detection framework for IoT-enabled mobile health informatics systems using deep learning. Inf Sci 662:120209","journal-title":"Inf Sci"},{"key":"11890_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2024.101939","author":"A Nazir","year":"2024","unstructured":"Nazir A et al (2024) Collaborative threat intelligence: enhancing IoT security through blockchain and machine learning integration. J King Saud Univ. https:\/\/doi.org\/10.1016\/j.jksuci.2024.101939","journal-title":"J King Saud Univ"},{"key":"11890_CR33","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.jpdc.2022.10.002","volume":"172","author":"P Kumar","year":"2023","unstructured":"Kumar P, Kumar R, Gupta GP, Tripathi R, Jolfaei A, Islam AKMN (2023) A blockchain-orchestrated deep learning approach for secure data transmission in IoT-enabled healthcare system. J Parallel Distrib Comput 172:69\u201383","journal-title":"J Parallel Distrib Comput"},{"key":"11890_CR34","doi-asserted-by":"publisher","first-page":"107630","DOI":"10.1016\/j.compbiomed.2023.107630","volume":"167","author":"W Moulahi","year":"2023","unstructured":"Moulahi W, Jdey I, Moulahi T, Alawida M, Alabdulatif A (2023) A blockchain-based federated learning mechanism for privacy preservation of healthcare IoT data. Comput Biol Med 167:107630","journal-title":"Comput Biol Med"},{"key":"11890_CR35","doi-asserted-by":"publisher","first-page":"100936","DOI":"10.1016\/j.iot.2023.100936","volume":"24","author":"SA Bakhsh","year":"2023","unstructured":"Bakhsh SA, Khan MA, Ahmed F, Alshehri MS, Ali H, Ahmad J (2023) Enhancing IoT network security through deep learning-powered intrusion detection system. Internet of Things 24:100936","journal-title":"Internet of Things"},{"issue":"4","key":"11890_CR36","doi-asserted-by":"publisher","first-page":"e4228","DOI":"10.1002\/ett.4228","volume":"32","author":"G Altan","year":"2021","unstructured":"Altan G (2021) SecureDeepNet\u2010IoT: a deep learning application for invasion detection in industrial Internet of things sensing systems. Trans Emerg Telecommun Technol 32(4):e4228","journal-title":"Trans Emerg Telecommun Technol"},{"key":"11890_CR37","doi-asserted-by":"publisher","first-page":"107689","DOI":"10.1016\/j.engappai.2023.107689","volume":"130","author":"N Guti\u00e9rrez","year":"2024","unstructured":"Guti\u00e9rrez N, Otero B, Rodr\u00edguez E, Utrera G, Mus S, Canal R (2024) A differential privacy protection-based federated deep learning framework to fog-embedded architectures. Eng Appl Artif Intell 130:107689","journal-title":"Eng Appl Artif Intell"},{"key":"11890_CR38","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s00779-021-01545-0","volume":"27","author":"K Owusu-Agyemeng","year":"2023","unstructured":"Owusu-Agyemeng K, Qin Z, Xiong H, Liu Y, Zhuang T, Qin Z (2023) MSDP: multi-scheme privacy-preserving deep learning via differential privacy. Pers Ubiquitous Comput. 27:221\u2013233","journal-title":"Pers Ubiquitous Comput."},{"key":"11890_CR39","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.future.2022.12.027","volume":"142","author":"J Zhang","year":"2023","unstructured":"Zhang J, Huang Q, Huang Y, Ding Q, Tsai P-W (2023) Dp-trajgan: a privacy-aware trajectory generation model with differential privacy. Futur Gener Comput Syst 142:25\u201340","journal-title":"Futur Gener Comput Syst"},{"key":"11890_CR40","unstructured":"Li X, Li Y, Yang H, Yang L, Liu XY (2019) DP-LSTM: differential privacy-inspired LSTM for stock prediction using financial news, arXiv Prepr. http:\/\/arxiv.org\/abs\/1912.10806"},{"key":"11890_CR41","doi-asserted-by":"crossref","unstructured":"Li W, Sang Y, Zhang M, Huang J, Cai C (2021) Traffic matrix prediction based on differential privacy and LSTM, In international conference on parallel and distributed computing: applications and technologies, Springer, pp 596\u2013603","DOI":"10.1007\/978-3-030-96772-7_56"},{"key":"11890_CR42","doi-asserted-by":"crossref","unstructured":"Adesuyi TA, Kim BM (2019) Preserving privacy in convolutional neural network: An\u220a-tuple differential privacy approach, In 2019 IEEE 2nd international conference on knowledge innovation and invention (ICKII), IEEE, pp 570\u2013573","DOI":"10.1109\/ICKII46306.2019.9042653"},{"issue":"12","key":"11890_CR43","doi-asserted-by":"publisher","first-page":"1375","DOI":"10.3390\/electronics10121375","volume":"10","author":"C Iwendi","year":"2021","unstructured":"Iwendi C, Anajemba JH, Biamba C, Ngabo D (2021) Security of things intrusion detection system for smart healthcare. Electronics 10(12):1375","journal-title":"Electronics"},{"key":"11890_CR44","doi-asserted-by":"publisher","first-page":"106576","DOI":"10.1109\/ACCESS.2020.3000421","volume":"8","author":"AA Hady","year":"2020","unstructured":"Hady AA, Ghubaish A, Salman T, Unal D, Jain R (2020) Intrusion detection system for healthcare systems using medical and network data: a comparison study. IEEE Access 8:106576\u2013106584","journal-title":"IEEE Access"},{"key":"11890_CR45","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.comcom.2020.05.048","volume":"160","author":"SP Rm","year":"2020","unstructured":"Rm SP et al (2020) An effective feature engineering for DNN using hybrid PCA-GWO for intrusion detection in IoMT architecture. Comput Commun 160:139\u2013149","journal-title":"Comput Commun"},{"key":"11890_CR46","doi-asserted-by":"publisher","first-page":"77396","DOI":"10.1109\/ACCESS.2020.2986013","volume":"8","author":"S Manimurugan","year":"2020","unstructured":"Manimurugan S, Al-Mutairi S, Aborokbah MM, Chilamkurti N, Ganesan S, Patan R (2020) Effective attack detection in internet of medical things smart environment using a deep belief neural network. IEEE Access 8:77396\u201377404","journal-title":"IEEE Access"},{"key":"11890_CR47","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.comcom.2021.01.013","volume":"170","author":"S Khan","year":"2021","unstructured":"Khan S, Akhunzada A (2021) A hybrid DL-driven intelligent SDN-enabled malware detection framework for Internet of Medical Things (IoMT). Comput Commun 170:209\u2013216","journal-title":"Comput Commun"},{"key":"11890_CR48","doi-asserted-by":"publisher","first-page":"824898","DOI":"10.3389\/fpubh.2021.824898","volume":"9","author":"M Akshay Kumaar","year":"2022","unstructured":"Akshay Kumaar M, Samiayya D, Vincent PMDR, Srinivasan K, Chang C-Y, Ganesh H (2022) A hybrid framework for intrusion detection in healthcare systems using deep learning. Front Public Health 9:824898","journal-title":"Front Public Health"},{"key":"11890_CR49","doi-asserted-by":"publisher","first-page":"105591","DOI":"10.1016\/j.engappai.2022.105591","volume":"117","author":"J Xie","year":"2023","unstructured":"Xie J, Sage M, Zhao YF (2023) Feature selection and feature learning in machine learning applications for gas turbines: a review. Eng Appl Artif Intell 117:105591","journal-title":"Eng Appl Artif Intell"},{"key":"11890_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41044-016-0014-0","volume":"1","author":"S Garc\u00eda","year":"2016","unstructured":"Garc\u00eda S, Ram\u00edrez-Gallego S, Luengo J, Ben\u00edtez JM, Herrera F (2016) Big data preprocessing: methods and prospects. Big Data Anal 1:1\u201322","journal-title":"Big Data Anal"},{"issue":"1","key":"11890_CR51","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.gltp.2022.04.020","volume":"3","author":"K Maharana","year":"2022","unstructured":"Maharana K, Mondal S, Nemade B (2022) A review: data pre-processing and data augmentation techniques. Global Transit Proc 3(1):91\u201399","journal-title":"Global Transit Proc"},{"key":"11890_CR52","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"key":"11890_CR53","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1016\/j.matpr.2021.06.016","volume":"51","author":"HS Gill","year":"2022","unstructured":"Gill HS, Khehra BS (2022) An integrated approach using CNN-RNN-LSTM for classification of fruit images. Mater Today Proc 51:591\u2013595","journal-title":"Mater Today Proc"},{"key":"11890_CR54","doi-asserted-by":"publisher","first-page":"102844","DOI":"10.1016\/j.jvcir.2020.102844","volume":"71","author":"J Cheng","year":"2020","unstructured":"Cheng J, Liu Y, Ma Y (2020) Protein secondary structure prediction based on integration of CNN and LSTM model. J Vis Commun Image Represent 71:102844","journal-title":"J Vis Commun Image Represent"},{"key":"11890_CR55","doi-asserted-by":"crossref","unstructured":"Phan N, Wu X, Hu H, Dou D (2017) Adaptive laplace mechanism: differential privacy preservation in deep learning, In 2017 IEEE international conference on data mining (ICDM), IEEE, pp 385\u2013394","DOI":"10.1109\/ICDM.2017.48"},{"key":"11890_CR56","unstructured":"Koufogiannis F, Han S, Pappas GJ (2015) Optimality of the laplace mechanism in differential privacy, arXiv Prepr. http:\/\/arxiv.org\/abs\/1504.00065, vol 10"},{"key":"11890_CR57","doi-asserted-by":"crossref","unstructured":"Zhang P et a. (2021) Dynamic access control technology based on zero-trust light verification network model, In 2021 international conference on communications, information system and computer engineering (CISCE), IEEE, pp 712\u2013715","DOI":"10.1109\/CISCE52179.2021.9445896"},{"issue":"1","key":"11890_CR58","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.bbe.2022.11.005","volume":"43","author":"IF Kilincer","year":"2023","unstructured":"Kilincer IF, Ertam F, Sengur A, Tan R-S, Acharya UR (2023) Automated detection of cybersecurity attacks in healthcare systems with recursive feature elimination and multilayer perceptron optimization. Biocybern Biomed Eng 43(1):30\u201341","journal-title":"Biocybern Biomed Eng"},{"key":"11890_CR59","doi-asserted-by":"publisher","first-page":"100658","DOI":"10.1016\/j.ijcip.2023.100658","volume":"44","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Zhu D, Wang M, Li J, Zhang J (2024) A comparative study of cyber security intrusion detection in healthcare systems. Int J Crit Infrastruct Prot 44:100658","journal-title":"Int J Crit Infrastruct Prot"},{"key":"11890_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103269","volume":"98","author":"W Caicedo-Torres","year":"2019","unstructured":"Caicedo-Torres W, Gutierrez J (2019) ISeeU: visually interpretable deep learning for mortality prediction inside the ICU. J Biomed Inform 98:103269","journal-title":"J Biomed Inform"},{"key":"11890_CR61","unstructured":"Ba J, Caruana R (2014) Do deep nets really need to be deep?. Adv Neural Inf Process Syst, vol. 27"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-11890-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-026-11890-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-11890-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T12:03:02Z","timestamp":1778328182000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-026-11890-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":61,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["11890"],"URL":"https:\/\/doi.org\/10.1007\/s00521-026-11890-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"20 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2026","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 there is no conflict of interest in research.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The results\/data\/figures in this manuscript have not been published elsewhere, nor are they under consideration by another publisher. All the material is owned by the authors, and\/or no permissions are required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"289"}}