{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T03:19:38Z","timestamp":1783567178126,"version":"3.55.0"},"reference-count":44,"publisher":"Ubiquity Press, Ltd.","funder":[{"name":"Science and Technology Planning Project of He’nan Province, China","award":["222102320343"],"award-info":[{"award-number":["222102320343"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8,31]]},"DOI":"10.5334\/dsj-2023-031","type":"journal-article","created":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T08:50:34Z","timestamp":1693471834000},"source":"Crossref","is-referenced-by-count":7,"title":["Enhancing Privacy-Preserving Intrusion Detection in Blockchain-Based Networks with Deep Learning"],"prefix":"10.5334","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1172-370X","authenticated-orcid":false,"given":"Junzhou","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3238-3847","authenticated-orcid":false,"given":"Qianhui","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8049-9317","authenticated-orcid":false,"given":"Feixian","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"3285","reference":[{"issue":"SI","key":"key20230831085018_B1","first-page":"1","article-title":"Cyber security against intrusion detection using ensemble-based approaches","volume":"2023","year":"2023","journal-title":"Security and Communication Networks"},{"key":"key20230831085018_B2","doi-asserted-by":"crossref","first-page":"105635","DOI":"10.1016\/j.cmpb.2020.105635","article-title":"Data preprocessing for heart diseaseclassification: A systematic literature review","volume":"195","year":"2020","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"key20230831085018_B3","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.jbi.2014.04.003","article-title":"Private predictive analysis on encrypted medical data","volume":"50","year":"2014","journal-title":"Journal of Biomedical Informatics"},{"key":"key20230831085018_B4","first-page":"1251","article-title":"Xception: Deep learning with depthwise separable convolutions","year":"2017"},{"key":"key20230831085018_B5","article-title":"Anonymizing data for privacy-preserving federated learning [online]","year":"2020"},{"issue":"1","key":"key20230831085018_B6","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s00607-020-00847-0","article-title":"Improving healthcare services using source anonymous scheme with privacy preserving distributed healthcare data collectionmining","volume":"103","year":"2021","journal-title":"Computing"},{"key":"key20230831085018_B7","first-page":"338","volume-title":"Differential privacy encyclopedia of cryptography and security","year":"2011"},{"key":"key20230831085018_B8","volume-title":"Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems","year":"2019"},{"key":"key20230831085018_B9","unstructured":"Google Colab. Available at https:\/\/colab.research.google.com\/ [Last accessed 02 May 2023]."},{"key":"key20230831085018_B10","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/j.ins.2019.01.070","article-title":"A privacy-preserving cryptosystem for IoT E-healthcare","volume":"527","year":"2020","journal-title":"Information Sciences"},{"key":"key20230831085018_B11","first-page":"630","article-title":"Identity mappings in deep residual networks","year":"2016"},{"key":"key20230831085018_B12","first-page":"770","article-title":"Deep residual learning for image recognition","year":"2016"},{"key":"key20230831085018_B13","year":"2017","journal-title":"Mobilenets: E\u2019cient convolutional neural networks for mobile vision applications"},{"key":"key20230831085018_B14","first-page":"4700","article-title":"Densely connected convolutional networks","year":"2017"},{"issue":"18","key":"key20230831085018_B15","doi-asserted-by":"crossref","first-page":"10057","DOI":"10.3390\/su131810057","article-title":"An Ensemble of a prediction and learning mechanism for improving accuracy of anomaly detection in network intrusion environments","volume":"13","year":"2021","journal-title":"Sustain"},{"key":"key20230831085018_B16","first-page":"507","volume-title":"Intelligent Data Engineering and Analytics","year":"2021"},{"key":"key20230831085018_B17","first-page":"25","article-title":"Imagenet classi\u00a6cation with deep convolutional neural networks","year":"2012","journal-title":"Advances in neural information processing systems"},{"key":"key20230831085018_B18","first-page":"265","article-title":"On optimization methods for deep learning","year":"2011"},{"key":"key20230831085018_B19","doi-asserted-by":"crossref","first-page":"1136","DOI":"10.1109\/JIOT.2021.3078543","article-title":"Decentralized federated learning via mutual knowledge transfer","volume":"9","year":"2022","journal-title":"IEEE Internet of Things Journal"},{"issue":"3","key":"key20230831085018_B20","first-page":"2021","article-title":"A federated learning based privacy-preserving smart healthcare system","volume":"18","year":"2021","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"5","key":"key20230831085018_B21","doi-asserted-by":"crossref","first-page":"773","DOI":"10.3390\/electronics9050773","article-title":"CrowdSFL: A secure crowd computing framework based on blockchain and federated learning","volume":"9","year":"2020","journal-title":"Electronics"},{"key":"key20230831085018_B22","article-title":"Federated learning: Collaborative machine learning without centralized training data","year":"2017","journal-title":"Google Research Blog"},{"key":"key20230831085018_B23","first-page":"8024","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume":"32","year":"2019","journal-title":"Advances in neural information processing systems"},{"issue":"3","key":"key20230831085018_B24","first-page":"2","article-title":"Privacy preserving blockchain with optimal deep learning model for smart cities","volume":"73","year":"2022","journal-title":"CMC"},{"key":"key20230831085018_B25","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1016\/j.future.2021.06.037","article-title":"Defending CNN against privacy leakage in edge computing via binary neural networks","volume":"125","year":"2021","journal-title":"Future Generation Computer Systems"},{"key":"key20230831085018_B26","article-title":"A Survey on federated learning: The journey from centralized to distributed on-site learning and beyond","year":"2020","journal-title":"IEEE Internet of Things Journal"},{"issue":"10","key":"key20230831085018_B27","doi-asserted-by":"crossref","first-page":"9530","DOI":"10.1109\/JIOT.2020.2991416","article-title":"Personalized federated learning with differential privacy","volume":"7","year":"2020","journal-title":"IEEE Internet of Things Journal"},{"key":"key20230831085018_B28","article-title":"A generic framework for privacy preserving deep learning [online]","year":"2018"},{"key":"key20230831085018_B29","first-page":"1","article-title":"Securing the IoT system of smart city against cyber threats using deep learning","volume":"2022","year":"2022","journal-title":"Discrete Dynamics in Nature and Society"},{"issue":"9","key":"key20230831085018_B30","doi-asserted-by":"crossref","first-page":"3270","DOI":"10.1109\/TKDE.2020.2964658","article-title":"Privacy preserving location data publishing: A machine learning approach","volume":"33","year":"2021","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"key20230831085018_B31","year":"2014","journal-title":"Very deep convolutional networks for large-scale image recognition"},{"key":"key20230831085018_B32","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.future.2021.11.028","article-title":"A framework for privacy-preservation of IoT healthcare data using Federated Learning and blockchain technology","volume":"129","year":"2022","journal-title":"Future Generation Computer Systems"},{"issue":"3","key":"key20230831085018_B33","doi-asserted-by":"crossref","first-page":"2326","DOI":"10.1109\/TNSE.2021.3089435","article-title":"PPSF: A privacy-preserving and secure framework using blockchain-based machinelearning for IoT-driven smart cities","volume":"8","year":"2021","journal-title":"IEEE Transactions on Network Science and Engineering"},{"key":"key20230831085018_B34","doi-asserted-by":"crossref","first-page":"152103","DOI":"10.1109\/ACCESS.2019.2947295","article-title":"Differential privacy for data and model publishing of medical ata","volume":"7","year":"2019","journal-title":"IEEE Access"},{"key":"key20230831085018_B35","first-page":"2818","article-title":"Rethinking the inception architecture for computer vision","year":"2016"},{"key":"key20230831085018_B36","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1109\/ACCESS.2019.2961372","article-title":"Machine learning adoption in blockchain-based smart applications: The challenges and a way forward","volume":"8","year":"2020","journal-title":"IEEE Access"},{"key":"key20230831085018_B37","doi-asserted-by":"crossref","first-page":"219744","DOI":"10.1109\/ACCESS.2020.3043037","article-title":"Blockchain-enabled federated learning with mechanism design","volume":"8","year":"2020","journal-title":"IEEE Access"},{"issue":"6","key":"key20230831085018_B38","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1109\/JSAC.2019.2904348","article-title":"Adaptive federated learning in resource constrained edge computing systems","volume":"37","year":"2019","journal-title":"IEEE Journal on Selected Areas in Communications"},{"issue":"5","key":"key20230831085018_B39","first-page":"2438","article-title":"DeepChain: Auditable and privacy-preserving deep learning with blockchain-based incentive","volume":"18","year":"2019","journal-title":"IEEE Transactions on Dependable and Secure Computing"},{"key":"key20230831085018_B40","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.future.2021.09.015","article-title":"An adaptive federated learning scheme with differential privacy preserving","volume":"127","year":"2022","journal-title":"Future Generation Computer Systems"},{"key":"key20230831085018_B41","first-page":"365","article-title":"Lung nodule detection and classification using 2D and 3D convolution neural networks (CNNs)","volume":"2021","year":"2021","journal-title":"Artificial Intelligence and Internet of Things"},{"issue":"3","key":"key20230831085018_B42","doi-asserted-by":"crossref","first-page":"2706","DOI":"10.1109\/TNSE.2021.3074185","article-title":"A privacy-preserving federated learning for multiparty data sharing in social IoTs","volume":"8","year":"2021","journal-title":"IEEE Transactions on Network Science and Engineering"},{"key":"key20230831085018_B43","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1016\/j.ins.2018.11.028","article-title":"Machine learning based privacy-preserving fair data trading in big data market","volume":"478","year":"2019","journal-title":"Informing Science"},{"key":"key20230831085018_B44","doi-asserted-by":"crossref","first-page":"48901","DOI":"10.1109\/ACCESS.2019.2909559","article-title":"Differential privacy preservation in deep learning: Challenges, opportunities and solutions","volume":"7","year":"2019","journal-title":"IEEE Access"}],"container-title":["Data Science Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/storage.googleapis.com\/jnl-up-j-dsj-files\/journals\/1\/articles\/1578\/64f08167ed146.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T10:43:16Z","timestamp":1782816196000},"score":1,"resource":{"primary":{"URL":"http:\/\/datascience.codata.org\/articles\/10.5334\/dsj-2023-031\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":44,"alternative-id":["10.5334\/dsj-2023-031"],"URL":"https:\/\/doi.org\/10.5334\/dsj-2023-031","relation":{},"ISSN":["1683-1470"],"issn-type":[{"value":"1683-1470","type":"print"}],"subject":[],"published":{"date-parts":[[2023]]},"article-number":"31"}}