{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:21:19Z","timestamp":1775067679141,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T00:00:00Z","timestamp":1709078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002563","name":"Sungshin Women\u2019s University","doi-asserted-by":"publisher","award":["H20220028"],"award-info":[{"award-number":["H20220028"]}],"id":[{"id":"10.13039\/501100002563","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>As the size of the IoT solutions and services market proliferates, industrial fields utilizing IoT devices are also diversifying. However, the proliferation of IoT devices, often intertwined with users\u2019 personal information and privacy, has led to a continuous surge in attacks targeting these devices. However, conventional network-level intrusion detection systems with pre-defined rulesets are gradually losing their efficacy due to the heterogeneous environments of IoT ecosystems. To address such security concerns, researchers have utilized ML-based network-level intrusion detection techniques. Specifically, transfer learning has been dedicated to identifying unforeseen malicious traffic in IoT environments based on knowledge distillation from the rich source domain data sets. Nevertheless, since most IoT devices operate in heterogeneous but small-scale environments, such as home networks, selecting adequate source domains for learning proves challenging. This paper introduces a framework designed to tackle this issue. In instances where assessing an adequate data set through pre-learning using transfer learning is non-trivial, our proposed framework advocates the selection of a data set as the source domain for transfer learning. This selection process aims to determine the appropriateness of implementing transfer learning, offering the best practice in such scenarios. Our evaluation demonstrates that the proposed framework successfully chooses a fitting source domain data set, delivering the highest accuracy.<\/jats:p>","DOI":"10.3390\/fi16030080","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T07:56:02Z","timestamp":1709106962000},"page":"80","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Transferable Deep Learning Framework for Improving the Accuracy of Internet of Things Intrusion Detection"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1376-6245","authenticated-orcid":false,"given":"Haedam","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Convergence Security Engineering, Sungshin Women\u2019s University, Seoul 02844, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7870-3588","authenticated-orcid":false,"given":"Suhyun","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Convergence Security Engineering, Sungshin Women\u2019s University, Seoul 02844, Republic of Korea"}]},{"given":"Hyemin","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Convergence Security Engineering, Sungshin Women\u2019s University, Seoul 02844, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6603-2920","authenticated-orcid":false,"given":"Jieun","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Convergence Security Engineering, Sungshin Women\u2019s University, Seoul 02844, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8183-0641","authenticated-orcid":false,"given":"Seongmin","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Convergence Security Engineering, Sungshin Women\u2019s University, Seoul 02844, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"22595","DOI":"10.1109\/JIOT.2022.3181582","article-title":"Secure infectious diseases detection system with iot-based e-health platforms","volume":"9","author":"Zhao","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1016\/j.procir.2016.05.050","article-title":"Integration of digital factory with smart factory based on Internet of Things","volume":"50","author":"Shariatzadeh","year":"2016","journal-title":"Procedia Cirp"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.comcom.2020.03.007","article-title":"Industrial control systems: Cyberattack trends and countermeasures","volume":"155","author":"Alladi","year":"2020","journal-title":"Comput. 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