{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T05:35:14Z","timestamp":1775367314965,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T00:00:00Z","timestamp":1638230400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Taif University Researchers Supporting Project, Taif University, Taif, Saudi Arabia","award":["TURSP-2020\/292"],"award-info":[{"award-number":["TURSP-2020\/292"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wireless Sensors Networks have been the focus of significant attention from research and development due to their applications of collecting data from various fields such as smart cities, power grids, transportation systems, medical sectors, military, and rural areas. Accurate and reliable measurements for insightful data analysis and decision-making are the ultimate goals of sensor networks for critical domains. However, the raw data collected by WSNs usually are not reliable and inaccurate due to the imperfect nature of WSNs. Identifying misbehaviours or anomalies in the network is important for providing reliable and secure functioning of the network. However, due to resource constraints, a lightweight detection scheme is a major design challenge in sensor networks. This paper aims at designing and developing a lightweight anomaly detection scheme to improve efficiency in terms of reducing the computational complexity and communication and improving memory utilization overhead while maintaining high accuracy. To achieve this aim, one-class learning and dimension reduction concepts were used in the design. The One-Class Support Vector Machine (OCSVM) with hyper-ellipsoid variance was used for anomaly detection due to its advantage in classifying unlabelled and multivariate data. Various One-Class Support Vector Machine formulations have been investigated and Centred-Ellipsoid has been adopted in this study due to its effectiveness. Centred-Ellipsoid is the most effective kernel among studies formulations. To decrease the computational complexity and improve memory utilization, the dimensions of the data were reduced using the Candid Covariance-Free Incremental Principal Component Analysis (CCIPCA) algorithm. Extensive experiments were conducted to evaluate the proposed lightweight anomaly detection scheme. Results in terms of detection accuracy, memory utilization, computational complexity, and communication overhead show that the proposed scheme is effective and efficient compared few existing schemes evaluated. The proposed anomaly detection scheme achieved the accuracy higher than 98%, with O(nd) memory utilization and no communication overhead.<\/jats:p>","DOI":"10.3390\/s21238017","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"8017","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine"],"prefix":"10.3390","volume":"21","author":[{"given":"Nurfazrina M.","family":"Zamry","sequence":"first","affiliation":[{"name":"School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Iskandar Puteri 81310, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anazida","family":"Zainal","sequence":"additional","affiliation":[{"name":"School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Iskandar Puteri 81310, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3558-6737","authenticated-orcid":false,"given":"Murad A.","family":"Rassam","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia"},{"name":"Faculty of Engineering and Information Technology, Taiz University, Taiz 6803, Yemen"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0412-7127","authenticated-orcid":false,"given":"Eman H.","family":"Alkhammash","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1468-0655","authenticated-orcid":false,"given":"Fuad A.","family":"Ghaleb","sequence":"additional","affiliation":[{"name":"School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Iskandar Puteri 81310, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2822-1708","authenticated-orcid":false,"given":"Faisal","family":"Saeed","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Taibah University, Medina 42353, Saudi Arabia"},{"name":"School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2787","DOI":"10.1016\/j.comnet.2010.05.010","article-title":"The Internet of Things: A survey","volume":"54","author":"Atzori","year":"2010","journal-title":"Comput. 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