AI-aided Hidden Camera Detection and Localization based on Raw IoT Network Traffic

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dc.contributor.authorLee, Jihyeonko
dc.contributor.authorSeo, Sangwonko
dc.contributor.authorYang, Taehunko
dc.contributor.authorPark, Soochangko
dc.date.accessioned2023-09-15T07:01:55Z-
dc.date.available2023-09-15T07:01:55Z-
dc.date.created2023-09-15-
dc.date.issued2022-09-
dc.identifier.citation47th IEEE Conference on Local Computer Networks, LCN 2022, pp.315 - 318-
dc.identifier.issn0742-1303-
dc.identifier.urihttp://hdl.handle.net/10203/312679-
dc.description.abstractThis paper proposes a novel scheme to detect and localize the spy cameras based on AI algorithm based raw traffic analytics, named AI-aided Hidden Camera Locator (AHCL). In AHCL, the video streaming data are filtered via the SVM (support vector machine) algorithm to quickly monitor whole raw network traffic from a router to the networks first. Then, gathered traffic data are denoised by the Denoising Autoencoder (DAE) technique to improve the data quality of classification for localization, where a camera transmits video streaming. Based on the proof-of-concept implementation, the proposed scheme can achieve 99.5% positioning accuracy of camera detection with the Ensemble Neural Networks (NNs).-
dc.languageEnglish-
dc.publisherIEEE Computer Society-
dc.titleAI-aided Hidden Camera Detection and Localization based on Raw IoT Network Traffic-
dc.typeConference-
dc.identifier.wosid000884366100051-
dc.identifier.scopusid2-s2.0-85143126099-
dc.type.rimsCONF-
dc.citation.beginningpage315-
dc.citation.endingpage318-
dc.citation.publicationname47th IEEE Conference on Local Computer Networks, LCN 2022-
dc.identifier.conferencecountryCN-
dc.identifier.conferencelocationEdmonton-
dc.identifier.doi10.1109/LCN53696.2022.9843203-
dc.contributor.localauthorLee, Jihyeon-
dc.contributor.nonIdAuthorSeo, Sangwon-
dc.contributor.nonIdAuthorYang, Taehun-
dc.contributor.nonIdAuthorPark, Soochang-
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