基于原始物联网网络流量的ai辅助隐藏摄像头检测与定位

Jihyeon Lee, Sangwon Seo, Taehun Yang, Soochang Park
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引用次数: 2

摘要

本文提出了一种基于原始流量分析的人工智能算法来检测和定位间谍摄像机的新方案,称为人工智能辅助隐藏摄像机定位器(AHCL)。在AHCL中,视频流数据通过支持向量机(SVM)算法进行过滤,以快速监控从路由器到网络的整个原始网络流量。然后,采集到的交通数据通过去噪自动编码器(DAE)技术进行去噪,以提高定位分类的数据质量,其中摄像机传输视频流。基于概念验证的实现,该方案使用集成神经网络(nn)可以实现99.5%的摄像机检测定位精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI-aided Hidden Camera Detection and Localization based on Raw IoT Network Traffic
This 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).
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