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Detecting Streaming Wireless Cameras...

Wu, Kevin.

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Detecting Streaming Wireless Cameras with Timing Analysis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Detecting Streaming Wireless Cameras with Timing Analysis./
作者:	Wu, Kevin.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	65 p.
附註:	Source: Masters Abstracts International, Volume: 80-02.
Contained By:	Masters Abstracts International80-02.
標題:	Computer Engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10826982
ISBN:	9780438175242

Detecting Streaming Wireless Cameras with Timing Analysis.
Wu, Kevin.

Detecting Streaming Wireless Cameras with Timing Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 65 p.

Source: Masters Abstracts International, Volume: 80-02.

Thesis (Master's)--University of Washington, 2018.

This item must not be added to any third party search indexes.

The Internet of Things (IoT) is growing rapidly thanks to the convenience it provides to users, as sensors collect, communicate, and collaborate with each other to provide better services. Wi-Fi cameras from a variety of manufacturers have been widely adopted to provide inexpensive monitoring services to general consumers. Although Wi-Fi cameras provide real-time monitoring, these devices often come with weak security mechanisms. This allows adversaries to exploit those IoT devices and have total control over with admin privileges. Moreover, those Wi-Fi cameras can be installed with bad intentions. Several incidents have been reported, where hidden Wi-Fi cameras are found in rental services such as Airbnb. To counter Wi-Fi camera spying and monitoring, we proposed a novel method to detect hidden Wi-Fi cameras, using timing analysis and a mobile phone as a detector. In order to provide constant and faster communication, IoT devices often required low-latency networks. Accordingly, the proposed methodology performs statistical analysis (Correlation Coefficient, Dynamic Time Warping, Kullback-Leibler divergence, and Jensen-Shannon divergence) to measure the similarity scores of network traffic streams and the recorded video from the mobile phone. Further, the similarity score is then used to identify hidden Wi-Fi cameras in the environment. The results of our experiments show that the proposed detection methodology can successfully discover hidden Wi-Fi cameras with an accuracy rate of 97.436%.

ISBN: 9780438175242Subjects--Topical Terms:

1567821
Computer Engineering.
Subjects--Index Terms:

Cybersecurity

Detecting Streaming Wireless Cameras with Timing Analysis.
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520 $a The Internet of Things (IoT) is growing rapidly thanks to the convenience it provides to users, as sensors collect, communicate, and collaborate with each other to provide better services. Wi-Fi cameras from a variety of manufacturers have been widely adopted to provide inexpensive monitoring services to general consumers. Although Wi-Fi cameras provide real-time monitoring, these devices often come with weak security mechanisms. This allows adversaries to exploit those IoT devices and have total control over with admin privileges. Moreover, those Wi-Fi cameras can be installed with bad intentions. Several incidents have been reported, where hidden Wi-Fi cameras are found in rental services such as Airbnb. To counter Wi-Fi camera spying and monitoring, we proposed a novel method to detect hidden Wi-Fi cameras, using timing analysis and a mobile phone as a detector. In order to provide constant and faster communication, IoT devices often required low-latency networks. Accordingly, the proposed methodology performs statistical analysis (Correlation Coefficient, Dynamic Time Warping, Kullback-Leibler divergence, and Jensen-Shannon divergence) to measure the similarity scores of network traffic streams and the recorded video from the mobile phone. Further, the similarity score is then used to identify hidden Wi-Fi cameras in the environment. The results of our experiments show that the proposed detection methodology can successfully discover hidden Wi-Fi cameras with an accuracy rate of 97.436%.
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