{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T20:13:23Z","timestamp":1783973603878,"version":"3.55.0"},"reference-count":32,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T00:00:00Z","timestamp":1771632000000},"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>Wireless sensor networks (WSNs) have become a critical component of modern Internet of Things (IoT) infrastructures; however, their constrained resources and distributed deployment expose them to various cyber threats. In this work, we present a machine learning-driven intrusion detection framework optimized for WSN-based IoT environments. The proposed approach employs the WSN-DS benchmark dataset and integrates adaptive synthetic sampling (ADASYN) to address class imbalance, followed by a hybrid feature selection strategy combining Feature Importance Selection (FIS) and Recursive Feature Elimination (RFE) to reduce dimensionality and improve learning efficiency. An XGBoost classifier is then trained using five-fold cross-validation to ensure robust generalization. The experimental results demonstrate that the proposed framework significantly outperforms baseline methods, achieving an overall accuracy of 99.87%, with substantial gains in terms of F1-score, precision, and recall. Comparative analysis against recent WSN-DS studies confirms the effectiveness of combining imbalance correction, optimized feature selection, and ensemble learning. These findings highlight the potential of the proposed model as a lightweight and highly accurate intrusion detection solution for emerging WSN-IoT deployments.<\/jats:p>","DOI":"10.3390\/fi18020113","type":"journal-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T08:58:21Z","timestamp":1771837101000},"page":"113","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Machine Learning-Driven Intrusion Detection for Securing IoT-Based Wireless Sensor Networks"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6057-9622","authenticated-orcid":false,"given":"Yirga Yayeh","family":"Munaye","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abebaw Demelash","family":"Gebeyehu","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Bahir Dar Institute of Technology, Bahir Dar 26, Ethiopia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7042-5109","authenticated-orcid":false,"given":"Li-Chia","family":"Tai","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zemenu Alem","family":"Abebe","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Bahir Dar Institute of Technology, Bahir Dar 26, Ethiopia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aeneas Bekele","family":"Workneh","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Bahir Dar Institute of Technology, Bahir Dar 26, Ethiopia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robel Berie","family":"Tarekegn","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yenework Belayneh","family":"Chekol","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Injibara University, Injibara 40, Ethiopia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5501-9871","authenticated-orcid":false,"given":"Getaneh Berie","family":"Tarekegn","sequence":"additional","affiliation":[{"name":"Independent Researcher, Silver Spring, MD 20904, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mansour, M., Gamal, A., Ahmed, A.I., Said, L.A., Elbaz, A., Herencs\u00e1r, N., and Soltan, A. 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