{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T23:03:12Z","timestamp":1773702192934,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,19]],"date-time":"2024-10-19T00:00:00Z","timestamp":1729296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The security of Wireless Sensor Networks (WSNs) is of the utmost importance because of their widespread use in various applications. Protecting WSNs from harmful activity is a vital function of intrusion detection systems (IDSs). An innovative approach to WSN intrusion detection (ID) utilizing the CatBoost classifier (Cb-C) and the Lyrebird Optimization Algorithm is presented in this work (LOA). As is typical in ID settings, Cb-C excels at handling datasets that are imbalanced. The lyrebird\u2019s remarkable capacity to imitate the sounds of its surroundings served as inspiration for the LOA, a metaheuristic optimization algorithm. The WSN-DS dataset, acquired from Prince Sultan University in Saudi Arabia, is used to assess the suggested method. Among the models presented, LOA-Cb-C produces the highest accuracy of 99.66%; nevertheless, when compared with the other methods discussed in this article, its error value of 0.34% is the lowest. Experimental results reveal that the suggested strategy improves WSN-IoT security over the existing methods in terms of detection accuracy and the false alarm rate.<\/jats:p>","DOI":"10.3390\/fi16100381","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T09:58:24Z","timestamp":1729504704000},"page":"381","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Securing the Edge: CatBoost Classifier Optimized by the Lyrebird Algorithm to Detect Denial of Service Attacks in Internet of Things-Based Wireless Sensor Networks"],"prefix":"10.3390","volume":"16","author":[{"given":"Sennanur Srinivasan","family":"Abinayaa","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, Dr. NGP Institute of Technology, Coimbatore 641048, India"}]},{"given":"Prakash","family":"Arumugam","sequence":"additional","affiliation":[{"name":"Karnavati School of Research, Karnavati University, Gujarat 382422, India"}]},{"given":"Divya Bhavani","family":"Mohan","sequence":"additional","affiliation":[{"name":"United World School of Computational Intelligence, Karnavati University, Gujarat 382422, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2031-0538","authenticated-orcid":false,"given":"Anand","family":"Rajendran","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru 560035, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4891-873X","authenticated-orcid":false,"given":"Abderezak","family":"Lashab","sequence":"additional","affiliation":[{"name":"Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2852-3204","authenticated-orcid":false,"given":"Baoze","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5236-4592","authenticated-orcid":false,"given":"Josep M.","family":"Guerrero","sequence":"additional","affiliation":[{"name":"Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,19]]},"reference":[{"key":"ref_1","first-page":"5143260","article-title":"Nonlinear Energy Optimization in the Wireless Sensor Network through NN-LEACH","volume":"2023","author":"Avinash","year":"2023","journal-title":"Math. 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