{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T00:18:01Z","timestamp":1781309881734,"version":"3.54.1"},"reference-count":34,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,7,2]],"date-time":"2020-07-02T00:00:00Z","timestamp":1593648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008982","name":"Qatar National Research Fund","doi-asserted-by":"publisher","award":["NPRP10-0101-170082"],"award-info":[{"award-number":["NPRP10-0101-170082"]}],"id":[{"id":"10.13039\/100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The number of Internet of Things (IoT) devices is growing at a fast pace in smart homes, producing large amounts of data, which are mostly transferred over wireless communication channels. However, various IoT devices are vulnerable to different threats, such as cyber-attacks, fluctuating network connections, leakage of information, etc. Statistical analysis and machine learning can play a vital role in detecting the anomalies in the data, which enhances the security level of the smart home IoT system which is the goal of this paper. This paper investigates the trustworthiness of the IoT devices sending house appliances\u2019 readings, with the help of various parameters such as feature importance, root mean square error, hyper-parameter tuning, etc. A spamicity score was awarded to each of the IoT devices by the algorithm, based on the feature importance and the root mean square error score of the machine learning models to determine the trustworthiness of the device in the home network. A dataset publicly available for a smart home, along with weather conditions, is used for the methodology validation. The proposed algorithm is used to detect the spamicity score of the connected IoT devices in the network. The obtained results illustrate the efficacy of the proposed algorithm to analyze the time series data from the IoT devices for spam detection.<\/jats:p>","DOI":"10.3390\/info11070344","type":"journal-article","created":{"date-parts":[[2020,7,3]],"date-time":"2020-07-03T06:51:20Z","timestamp":1593759080000},"page":"344","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Ensemble-Based Spam Detection in Smart Home IoT Devices Time Series Data Using Machine Learning Techniques"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3754-4162","authenticated-orcid":false,"given":"Ameema","family":"Zainab","sequence":"first","affiliation":[{"name":"Electrical and Computer Engineering, Texas A&amp;M University, College Station, TX 77843, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9392-6141","authenticated-orcid":false,"given":"Shady","family":"S. Refaat","sequence":"additional","affiliation":[{"name":"Electrical &amp; Computer Engineering, Texas A&amp;M University at Qatar, Doha 23874, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Othmane","family":"Bouhali","sequence":"additional","affiliation":[{"name":"Research Computing, Texas A&amp;M University at Qatar, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha 5825, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,2]]},"reference":[{"key":"ref_1","unstructured":"(2020, April 30). Chapter 19: Admission Control-Based Load Protection in the Smart Grid\u2014Security and Privacy in Cyber-Physical Systems. Available online: https:\/\/learning.oreilly.com\/library\/view\/security-and-privacy\/9781119226048\/c19.xhtml."},{"key":"ref_2","unstructured":"(2020, May 05). 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