Please use this identifier to cite or link to this item: https://doi.org/10.1145/3317549.3324928
Title: SurFi: Detecting surveillance camera looping attacks with wi-fi channel state information
Authors: Lakshmanan, N
Bang, I 
Kang, MS 
Han, J 
Lee, JT
Keywords: cs.CR
cs.CR
Issue Date: 15-May-2019
Publisher: ACM Press
Citation: Lakshmanan, N, Bang, I, Kang, MS, Han, J, Lee, JT (2019-05-15). SurFi: Detecting surveillance camera looping attacks with wi-fi channel state information. WiSec 2019 - Proceedings of the 2019 Conference on Security and Privacy in Wireless and Mobile Networks : 239-244. ScholarBank@NUS Repository. https://doi.org/10.1145/3317549.3324928
Abstract: © 2019 Association for Computing Machinery. The proliferation of surveillance cameras has greatly improved the physical security of many security-critical properties including buildings, stores, and homes. However, recent surveil- lance camera looping attacks demonstrate new security threats- adversaries can replay a seemingly benign video feed of a place of interest while trespassing or stealing valuables without getting caught. Unfortunately, such attacks are extremely difficult to detect in real-time due to cost and implementation constraints. In this paper, we propose SurFi to detect these attacks in real-time by utilizing commonly available Wi-Fi signals. In particular, we leverage that channel state information (CSI) from Wi-Fi signals also perceives human activities in the place of interest in addition to surveillance cameras. SurFi processes and correlates the live video feeds and theWi-Fi CSI signals to detect any mismatches that would identify the presence of the surveillance camera looping attacks. SurFi does not require the deployment of additional infrastructure because Wi-Fi transceivers are easily found in the urban indoor environment. We design and implement the SurFi system and evaluate its effectiveness in detecting surveillance camera looping attacks. Our evaluation demonstrates that SurFi effectively identifies attacks with up to an attack detection accuracy of 98.8% and 0.1% false positive rate.
Source Title: WiSec 2019 - Proceedings of the 2019 Conference on Security and Privacy in Wireless and Mobile Networks
URI: https://scholarbank.nus.edu.sg/handle/10635/156804
ISBN: 9781450367264
DOI: 10.1145/3317549.3324928
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