{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T03:02:00Z","timestamp":1768878120488,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T00:00:00Z","timestamp":1692921600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Saud University, Riyadh, Saudi Arabia","award":["RSP2023R102"],"award-info":[{"award-number":["RSP2023R102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The rapid growth of the Internet of Things (IoT) and big data has raised security concerns. Protecting IoT big data from attacks is crucial. Detecting real-time network attacks efficiently is challenging, especially in the resource-limited IoT setting. To enhance IoT security, intrusion detection systems using traffic features have emerged. However, these face difficulties due to varied traffic feature formats, hindering fast and accurate detection model training. To tackle accuracy issues caused by irrelevant features, a new model, LVW-MECO (LVW enhanced with multiple evaluation criteria), is introduced. It uses the LVW (Las Vegas Wrapper) algorithm with multiple evaluation criteria to identify pertinent features from IoT network data, boosting intrusion detection precision. Experimental results confirm its efficacy in addressing IoT security problems. LVW-MECO enhances intrusion detection performance and safeguards IoT data integrity, promoting a more secure IoT environment.<\/jats:p>","DOI":"10.3390\/s23177434","type":"journal-article","created":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T06:10:22Z","timestamp":1693203022000},"page":"7434","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Multi-Criteria Feature Selection Based Intrusion Detection for Internet of Things Big Data"],"prefix":"10.3390","volume":"23","author":[{"given":"Jie","family":"Wang","sequence":"first","affiliation":[{"name":"School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Xuanrui","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7191-1993","authenticated-orcid":false,"given":"Gaosheng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Ruiqi","family":"Ouyang","sequence":"additional","affiliation":[{"name":"School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Yunli","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Software, Dalian University of Technology, Dalian 116024, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6111-8617","authenticated-orcid":false,"given":"Osama","family":"Alfarraj","sequence":"additional","affiliation":[{"name":"Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9225","DOI":"10.1109\/TVT.2022.3176243","article-title":"RMGen: A Tri-Layer Vehicular Trajectory Data Generation Model Exploring Urban Region Division and Mobility Pattern","volume":"71","author":"Kong","year":"2022","journal-title":"IEEE Trans. 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