{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:28:30Z","timestamp":1776443310174,"version":"3.51.2"},"reference-count":39,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T00:00:00Z","timestamp":1626739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF) grant 487 funded by the Korea government (MSIT)","award":["(No.2020R1A2B5B01001758)"],"award-info":[{"award-number":["(No.2020R1A2B5B01001758)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, there is an exponential explosion of data generation, collection, and processing in computer networks. With this expansion of data, network attacks have also become a congenital problem in complex networks. The resource utilization, complexity, and false alarm rates are major challenges in current Network Intrusion Detection Systems (NIDS). The data fusion technique is an emerging technology that merges data from multiple sources to form more certain, precise, informative, and accurate data. Moreover, most of the earlier intrusion detection models suffer from overfitting problems and lack optimal detection of intrusions. In this paper, we propose a multi-source data fusion scheme for intrusion detection in networks (MIND) , where data fusion is performed by the horizontal emergence of two datasets. For this purpose, the Hadoop MapReduce tool such as, Hive is used. In addition, a machine learning ensemble classifier is used for the fused dataset with fewer parameters. Finally, the proposed model is evaluated with a 10-fold-cross validation technique. The experiments show that the average accuracy, detection rate, false positive rate, true positive rate, and F-measure are 99.80%, 99.80%, 0.29%, 99.85%, and 99.82% respectively. Moreover, the results indicate that the proposed model is significantly effective in intrusion detection compared to other state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/s21144941","type":"journal-article","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T11:26:10Z","timestamp":1626780370000},"page":"4941","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["MIND: A Multi-Source Data Fusion Scheme for Intrusion Detection in Networks"],"prefix":"10.3390","volume":"21","author":[{"given":"Naveed","family":"Anjum","sequence":"first","affiliation":[{"name":"Department of Computing, Riphah International University, Faisalabad 38000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5373-8063","authenticated-orcid":false,"given":"Zohaib","family":"Latif","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hanyang University, Seoul 04763, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6564-2392","authenticated-orcid":false,"given":"Choonhwa","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hanyang University, Seoul 04763, Korea"}]},{"given":"Ijaz Ali","family":"Shoukat","sequence":"additional","affiliation":[{"name":"Department of Computing, Riphah International University, Faisalabad 38000, Pakistan"}]},{"given":"Umer","family":"Iqbal","sequence":"additional","affiliation":[{"name":"Department of Computing, Riphah International University, Faisalabad 38000, Pakistan"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107840","DOI":"10.1016\/j.comnet.2021.107840","article-title":"Machine learning methods for cyber security intrusion detection: Datasets and comparative study","volume":"188","author":"Kilincer","year":"2021","journal-title":"Comput. 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