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Despite their widespread use in intrusion detection against DDoS attacks, machine learning methods still suffer accuracy degradation due to inadequate data pre-processing and computational inefficiency. This study combined a fine-tuned extreme gradient boosting (XGBoost) model with correlation-based feature selection\u2014for efficient feature selection\u2014to effectively maximize detection accuracy while lowering computing overhead. Both correlation-based feature selection and XGBoost contribute to boosting the final model's efficiency. To evaluate the proposed model, different metrics were employed over three DDoS data sets, considering both binary and multi-classification scenarios. Experimental findings demonstrate that the proposed XGBoost achieves highly competitive accuracy. For the Network Security Laboratory\u2013Knowledge Discovery Databases data set, University of New South Wales\u2013Network Behavior15 data set, and Canadian Institute for Cybersecurity\u2013Intrusion Detection System\u20132017 data set, the model secures 0.995, 1.000, and 0.999 and 0.996, 0.885, 0.998 for binary and multi-classification, respectively, outperforming its rival models.<\/p>","DOI":"10.4018\/ijswis.383062","type":"journal-article","created":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T18:01:11Z","timestamp":1751479271000},"page":"1-39","source":"Crossref","is-referenced-by-count":4,"title":["Boosting Intrusion Detection Against DDoS Attacks Using a Feature Engineering-Based Fine-Tuned XGBoost Model"],"prefix":"10.4018","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8903-6944","authenticated-orcid":true,"given":"Mohammad","family":"Nassef","sequence":"first","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJSWIS.383062-0","doi-asserted-by":"publisher","DOI":"10.1109\/NISS55057.2022.10085013"},{"key":"IJSWIS.383062-1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.126942"},{"key":"IJSWIS.383062-2","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-023-00849-7"},{"key":"IJSWIS.383062-3","doi-asserted-by":"publisher","DOI":"10.3390\/electronics9122152"},{"key":"IJSWIS.383062-4","doi-asserted-by":"publisher","DOI":"10.4018\/IJSWIS.297032"},{"key":"IJSWIS.383062-5","doi-asserted-by":"publisher","DOI":"10.3390\/a17120550"},{"key":"IJSWIS.383062-6","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2024.104222"},{"key":"IJSWIS.383062-7","doi-asserted-by":"publisher","DOI":"10.4018\/IJSWIS.346377"},{"key":"IJSWIS.383062-8","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-025-01137-2"},{"key":"IJSWIS.383062-9","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2025.103770"},{"key":"IJSWIS.383062-10","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2025.3542845"},{"key":"IJSWIS.383062-11","unstructured":"Brownlee, J. 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