{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:44:23Z","timestamp":1760147063656,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,3]],"date-time":"2023-01-03T00:00:00Z","timestamp":1672704000000},"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>One possible device authentication method is based on device fingerprints, such as software- or hardware-based unique characteristics. In this paper, we propose a fingerprinting technique based on passive externally measured information, i.e., current consumption from the electrical network. The key insight is that small hardware discrepancies naturally exist even between same-electrical-circuit devices, making it feasible to identify slight variations in the consumed current under steady-state conditions. An experimental database of current consumption signals of two similar groups containing 20 same-model computer displays was collected. The resulting signals were classified using various state-of-the-art time-series classification (TSC) methods. We successfully identified 40 similar (same-model) electrical devices with about 94% precision, while most errors were concentrated in confusion between a small number of devices. A simplified empirical wavelet transform (EWT) paired with a linear discriminant analysis (LDA) classifier was shown to be the recommended classification method.<\/jats:p>","DOI":"10.3390\/s23010533","type":"journal-article","created":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T03:27:44Z","timestamp":1672802864000},"page":"533","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Passive Fingerprinting of Same-Model Electrical Devices by Current Consumption"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5522-988X","authenticated-orcid":false,"given":"Mikhail","family":"Ronkin","sequence":"first","affiliation":[{"name":"Engineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University, 620078 Yekaterinburg, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4468-7791","authenticated-orcid":false,"given":"Dima","family":"Bykhovsky","sequence":"additional","affiliation":[{"name":"Electrical and Electronics Engineering Department, Shamoon College of Engineering, Beer-Sheva 8410802, Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1109\/TDSC.2005.26","article-title":"Remote physical device fingerprinting","volume":"2","author":"Kohno","year":"2005","journal-title":"IEEE Trans. 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