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.
|Title of host publication||WiSec 2019 - Proceedings of the 2019 Conference on Security and Privacy in Wireless and Mobile Networks|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||6|
|Publication status||Published - 2019 May 15|
|Event||12th Conference on Security and Privacy in Wireless and Mobile Networks, WiSec 2019 - Miami, United States|
Duration: 2019 May 15 → 2019 May 17
|Name||WiSec 2019 - Proceedings of the 2019 Conference on Security and Privacy in Wireless and Mobile Networks|
|Conference||12th Conference on Security and Privacy in Wireless and Mobile Networks, WiSec 2019|
|Period||19/5/15 → 19/5/17|
Bibliographical noteFunding Information:
We thank the anonymous reviewers of this paper for their helpful feedback. This research was partially supported by the grants from Singapore Ministry of Education Academic Research Fund Tier-1 (R-252-000-690-114, R-252-000-A26-133).
© 2019 Association for Computing Machinery.
All Science Journal Classification (ASJC) codes
- Safety, Risk, Reliability and Quality
- Computer Networks and Communications