https://scholars.lib.ntu.edu.tw/handle/123456789/607270
Title: | Physical tampering detection using single cots wi?fi endpoint | Authors: | Chan P.Y Lai A.I.-C PEI-YUAN WU RUEY-BEEI WU |
Keywords: | Channel state information (CSI);COTS Wi?Fi mobile device;Deep neural network (DNN);Physical tampering detection;Single embedded antenna;Antennas;Commercial off-the-shelf;Deep neural networks;Embedded antenna;False positive rates;Subcarrier;Tampering detection;True positive rates;Channel state information;article;deep neural network;positivity rate;preliminary data;Neural Networks, Computer | Issue Date: | 2021 | Journal Volume: | 21 | Journal Issue: | 16 | Source: | Sensors | Abstract: | This paper proposes a practical physical tampering detection mechanism using inexpen-sive commercial off?the?shelf (COTS) Wi?Fi endpoint devices with a deep neural network (DNN) on channel state information (CSI) in the Wi?Fi signals. Attributed to the DNN that identifies physical tampering events due to the multi?subcarrier characteristics in CSI, our methodology takes effect using only one COTS Wi?Fi endpoint with a single embedded antenna to detect changes in the rel-ative orientation between the Wi?Fi infrastructure and the endpoint, in contrast to previous sophis-ticated, proprietary approaches. Preliminary results show that our detectors manage to achieve a 95.89% true positive rate (TPR) with no worse than a 4.12% false positive rate (FPR) in detecting physical tampering events. ? 2021 by the authors. Licensee MDPI, Basel, Switzerland. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113716816&doi=10.3390%2fs21165665&partnerID=40&md5=f9cd60034abaf85c0960394a9aaa032f https://scholars.lib.ntu.edu.tw/handle/123456789/607270 |
ISSN: | 14248220 | DOI: | 10.3390/s21165665 |
Appears in Collections: | 電機工程學系 |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.