{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:38:27Z","timestamp":1760240307781,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,5,5]],"date-time":"2019-05-05T00:00:00Z","timestamp":1557014400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001700","name":"Ministry of Education, Culture, Sports, Science and Technology","doi-asserted-by":"publisher","award":["16K00335"],"award-info":[{"award-number":["16K00335"]}],"id":[{"id":"10.13039\/501100001700","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61803096"],"award-info":[{"award-number":["61803096"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Device-free localization (DFL) locates targets without being equipped with the attached devices, which is of great significance for intrusion detection or monitoring in the era of the Internet-of-Things (IoT). Aiming at solving the problems of low accuracy and low robustness in DFL approaches, in this paper, we first treat the RSS signal as an RSS-image matrix and conduct a process of eliminating the background to dig out the variation component with distinguished features. Then, we make use of these feature-rich images by formulating DFL as an image classification problem. Furthermore, a deep convolutional neural network (CNN) is designed to extract features automatically for classification. The localization performance of the proposed background elimination-based CNN (BE-CNN) scheme is validated with a real-world dataset of outdoor DFL. In addition, we also validate the robust performance of the proposal by conducting numerical experiments with different levels of noise. Experimental results demonstrate that the proposed scheme has an obvious advantage in terms of improving localization accuracy and robustness for DFL. Particularly, the BE-CNN can maintain the highest localization accuracy of 100%, even in noisy conditions when the SNR is over \u22125 dB. The BE-based methods can outperform all the corresponding raw data-based methods in terms of the localization accuracy. In addition, the proposed method can outperform the comparison methods, deep neural network with autoencoder, K-nearest-neighbor (KNN), support vector machines (SVM), etc., in terms of the localization accuracy and robustness.<\/jats:p>","DOI":"10.3390\/sym11050630","type":"journal-article","created":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T11:22:35Z","timestamp":1557400955000},"page":"630","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Intrusion Detection Based on Device-Free Localization in the Era of IoT"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2369-8862","authenticated-orcid":false,"given":"Lingjun","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu, Fukushima 965-0006, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunhua","family":"Su","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu, Fukushima 965-0006, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2853-8892","authenticated-orcid":false,"given":"Huakun","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu, Fukushima 965-0006, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoyang","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu, Fukushima 965-0006, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuxue","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu, Fukushima 965-0006, Japan"},{"name":"School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu, Fukushima 965-0006, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/MC.2018.3011034","article-title":"Intrusion detection in the era of IoT: Building trust via traffic filtering and sampling","volume":"51","author":"Meng","year":"2018","journal-title":"Computer"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/COMST.2015.2402161","article-title":"Intrusion detection in 802.11 networks: Empirical evaluation of threats and a public dataset","volume":"18","author":"Kolias","year":"2016","journal-title":"IEEE Commun. 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