{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:33:32Z","timestamp":1774539212638,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Recent advancements across various sectors have resulted in a significant increase in the utilization of smart gadgets. This augmentation has resulted in an expansion of the network and the devices linked to it. Nevertheless, the development of the network has concurrently resulted in a rise in policy infractions impacting information security. Finding intruders immediately is a critical component of maintaining network security. The intrusion detection system is useful for network security because it can quickly identify threats and give alarms. In this paper, a new approach for network intrusion detection was proposed. Combining the results of machine learning models like the random forest, decision tree, k-nearest neighbors, and XGBoost with logistic regression as a meta-model is what this method is based on. For the feature selection technique, the proposed approach creates an advanced method that combines the correlation-based feature selection with an embedded technique based on XGBoost. For handling the challenge of an imbalanced dataset, a SMOTE-TOMEK technique is used. The suggested algorithm is tested on the NSL-KDD and CIC-IDS datasets. It shows a high performance with an accuracy of 99.99% for both datasets. These results prove the effectiveness of the proposed approach.<\/jats:p>","DOI":"10.3390\/computers14030082","type":"journal-article","created":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T10:55:48Z","timestamp":1740480948000},"page":"82","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Ensemble Learning for Network Intrusion Detection Based on Correlation and Embedded Feature Selection Techniques"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9434-2914","authenticated-orcid":false,"given":"Ghalia","family":"Nassreddine","sequence":"first","affiliation":[{"name":"Computer and Information Systems Department, Rafik Hariri University, Damour-Chouf 2010, Lebanon"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6748-7368","authenticated-orcid":false,"given":"Mohamad","family":"Nassereddine","sequence":"additional","affiliation":[{"name":"School of Engineering, University of Wollongong in Dubai, Knowledge Village, Dubai P.O. Box 20183, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9473-2365","authenticated-orcid":false,"given":"Obada","family":"Al-Khatib","sequence":"additional","affiliation":[{"name":"School of Engineering, University of Wollongong in Dubai, Knowledge Village, Dubai P.O. Box 20183, United Arab Emirates"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,25]]},"reference":[{"key":"ref_1","unstructured":"Coker, J. (2025, February 03). Top 10 Cyber-Attacks of 2023. Available online: https:\/\/www.infosecurity-magazine.com\/news-features\/top-cyber-attacks-2023\/."},{"key":"ref_2","unstructured":"Governance, I. (2025, February 03). List of Data Breaches and Cyber Attacks in 2023\u20148,214,886,660 Records Breached. 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