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The major challenge was to develop a non-faulty framework to detect meddling (to overcome the traditional ways). With the development of machine learning technology, detecting and stopping the meddling process in the early stages is much easier. In this study, the proposed framework uses numerous data collection and processing techniques and machine learning techniques to train the meddling data and detect anomalies. The proposed framework uses support vector machine (SVM) and K-nearest neighbor (KNN) machine learning algorithms to detect the meddling in a network entangled with blockchain technology to ensure the privacy and protection of models as well as communication data. SVM achieves the highest training detection accuracy (DA) and misclassification rate (MCR) of 99.59% and 0.41%, respectively, and SVM achieves the highest-testing DA and MCR of 99.05% and 0.95%, respectively. The presented framework portrays the best meddling detection results, which are very helpful for various communication and transaction processes.<\/jats:p>","DOI":"10.3390\/s22186755","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"6755","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Network Meddling Detection Using Machine Learning Empowered with Blockchain Technology"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1443-8065","authenticated-orcid":false,"given":"Muhammad Umar","family":"Nasir","sequence":"first","affiliation":[{"name":"Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Safiullah","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of IT Convergence Engineering, Gachon University, Seongnam 13120, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shahid","family":"Mehmood","sequence":"additional","affiliation":[{"name":"Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9789-5231","authenticated-orcid":false,"given":"Muhammad Adnan","family":"Khan","sequence":"additional","affiliation":[{"name":"Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam 13557, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3999-6581","authenticated-orcid":false,"given":"Muhammad","family":"Zubair","sequence":"additional","affiliation":[{"name":"Faculty of Computing, Riphah International University, Islamabad Campus, Islamabad 45000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4240-6255","authenticated-orcid":false,"given":"Seong Oun","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Seongnam 13120, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"41525","DOI":"10.1109\/ACCESS.2019.2895334","article-title":"Deep Learning Approach for Intelligent Intrusion Detection System","volume":"7","author":"Vinayakumar","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TDSC.2012.20","article-title":"Detecting and Resolving Firewall Policy Anomalies","volume":"9","author":"Hu","year":"2012","journal-title":"IEEE Trans. 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