{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:16:34Z","timestamp":1772907394086,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The Internet of Things (IoT) refers to a system of interconnected, internet-connected devices and sensors that allows the collection and dissemination of data. The data provided by these sensors may include outliers or exhibit anomalous behavior as a result of attack activities or device failure, for example. However, the majority of existing outlier detection algorithms rely on labeled data, which is frequently hard to obtain in the IoT domain. More crucially, the IoT\u2019s data volume is continually increasing, necessitating the requirement for predicting and identifying the classes of future data. In this study, we propose an unsupervised technique based on a deep Variational Auto-Encoder (VAE) to detect outliers in IoT data by leveraging the characteristic of the reconstruction ability and the low-dimensional representation of the input data\u2019s latent variables of the VAE. First, the input data are standardized. Then, we employ the VAE to find a reconstructed output representation from the low-dimensional representation of the latent variables of the input data. Finally, the reconstruction error between the original observation and the reconstructed one is used as an outlier score. Our model was trained only using normal data with no labels in an unsupervised manner and evaluated using Statlog (Landsat Satellite) dataset. The unsupervised model achieved promising and comparable results with the state-of-the-art outlier detection schemes with a precision of \u224890% and an F1 score of 79%.<\/jats:p>","DOI":"10.3390\/s22176617","type":"journal-article","created":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T00:19:01Z","timestamp":1662077941000},"page":"6617","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Unsupervised Outlier Detection in IOT Using Deep VAE"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6615-7658","authenticated-orcid":false,"given":"Walaa","family":"Gouda","sequence":"first","affiliation":[{"name":"Department of Computer Engineering and Network, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Al Jouf, Saudi Arabia"},{"name":"Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo 13518, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5497-9320","authenticated-orcid":false,"given":"Sidra","family":"Tahir","sequence":"additional","affiliation":[{"name":"University Institute of Information Technology, PMAS Arid Agricultural University, Rawalpindi 46000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1714-1948","authenticated-orcid":false,"given":"Saad","family":"Alanazi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Al Jouf, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6613-0831","authenticated-orcid":false,"given":"Maram","family":"Almufareh","sequence":"additional","affiliation":[{"name":"Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Al Jouf, Saudi Arabia"}]},{"given":"Ghadah","family":"Alwakid","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Al Jouf, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s42979-020-00195-y","article-title":"Development of smart healthcare monitoring system in IoT environment","volume":"1","author":"Islam","year":"2020","journal-title":"SN Comput. 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