{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T16:33:13Z","timestamp":1771518793738,"version":"3.50.1"},"reference-count":20,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T00:00:00Z","timestamp":1696809600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Council of Canada\u2019s Aging in Place Program","award":["AiP-032"],"award-info":[{"award-number":["AiP-032"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCP"],"abstract":"<jats:p>To address the lack of datasets for agetech, this paper presents an approach for generating synthetic datasets that include traces of benign and attack datasets for agetech. The generated datasets could be used to develop and evaluate intrusion detection systems for smart homes for seniors aging in place. After reviewing several resources, it was established that there are no agetech attack data for sensor readings. Therefore, in this research, several methods for generating attack data were explored using attack data patterns from an existing IoT dataset called TON_IoT weather data. The TON_IoT dataset could be used in different scenarios, but in this study, the focus is to apply it to agetech. The attack patterns were replicated in a normal agetech dataset from a temperature sensor collected from the Information Security and Object Technology (ISOT) research lab. The generated data are different from normal data, as abnormal segments are shown that could be considered as attacks. The generated agetech attack datasets were also trained using machine learning models, and, based on different metrics, achieved good classification performance in predicting whether a sample is benign or malicious.<\/jats:p>","DOI":"10.3390\/jcp3040033","type":"journal-article","created":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T07:37:50Z","timestamp":1696837070000},"page":"744-757","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Framework for Synthetic Agetech Attack Data Generation"],"prefix":"10.3390","volume":"3","author":[{"given":"Noel","family":"Khaemba","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8P 5C2, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Issa","family":"Traor\u00e9","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8P 5C2, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4045-8687","authenticated-orcid":false,"given":"Mohammad","family":"Mamun","sequence":"additional","affiliation":[{"name":"National Research Council of Canada, Government of Canada, Ottawa, ON K1A 0R6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,9]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2022). 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