LeakyPick: IoT Audio Spy Detector

Mitev, Richard; Pazii, Anna; Miettinen, Markus; Enck, William; Sadeghi, Ahmad‐Reza

doi:10.1145/3427228.3427277

Cited by 28 publications

(14 citation statements)

References 17 publications

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“…Hidden Wireless Camera Detection: There are emerging research efforts performing hidden wireless camera detection due to their popularity and the privacy concerns associated with unauthorized videotaping [9,10,22,24,29,39]. For example, [10] proposes a hidden wireless camera detection approach by utilizing the intrinsic traffic patterns of flows from wireless cameras.…”

Section: Related Workmentioning

confidence: 99%

“…[24] investigates the responsive traffic variation corresponding to the light condition change to determine whether the traffic is produced by a wireless camera. [29] proposes to detect wireless cameras by monitoring network traffic that indicates the characteristics of corresponding audio transmission. All those traffic pattern based techniques, however, can only detect the existence of traffic flows belonging to wireless cameras, and they cannot tell the exact location of the camera.…”

Section: Related Workmentioning

confidence: 99%

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MotionCompass

Fang

et al. 2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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Wireless security cameras are integral components of security systems used by military installations, corporations, and, due to their increased affordability, many private homes. These cameras commonly employ motion sensors to identify that something is occurring in their fields of vision before starting to record and notifying the property owner of the activity. In this paper, we discover that the motion sensing action can disclose the location of the camera through a novel wireless camera localization technique we call MotionCompass. In short, a user who aims to avoid surveillance can find a hidden camera by creating motion stimuli and sniffing wireless traffic for a response to that stimuli. With the motion trajectories within the motion detection zone, the exact location of the camera can be then computed. We develop an Android app to implement MotionCompass. Our extensive experiments using the developed app and 18 popular wireless security cameras demonstrate that for cameras with one motion sensor, MotionCompass can attain a mean localization error of around 5 cm with less than 140 seconds. This localization technique builds upon existing work that detects the existence of hidden cameras, to pinpoint their exact location and area of surveillance. CCS CONCEPTS• Security and privacy → Mobile and wireless security; Privacy protections.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

MotionCompass

Fang

et al. 2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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“…We assume that the adversaries conduct attacks in the WLAN, and the specific behaviors can be (1) connecting unauthorized devices to the wireless network such as hidden cameras and bugs [24,47]; (2) using IoT honeypots or virtual machines to forge legitimate real devices [48,49]; (3) utilizing software to counterfeit the wireless AP, which has the same SSID and MAC address as the legitimate one, deceiving the user to connect and further performing malicious attacks [17][18][19][20]; (4) exploiting the device vulnerabilities that have been disclosed on the Internet to endanger the security of user devices and the entire platform [37,50].…”

Section: Reat Modelmentioning

confidence: 99%

WND-Identifier: Automated and Efficient Identification of Wireless Network Devices

Zhu

Liu

et al. 2021

Security and Communication Networks

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The widespread application of wireless communication technology brings great convenience to people, but security and privacy problems also arise. To assess and guarantee the security of wireless networks and user devices, discovering and identifying wireless devices become a foremost task. Currently, effective device identification is still a challenging issue, as device fingerprinting requires huge training datasets and is difficult to expand, and rule-based identification is not accurate and reliable enough. In this paper, we propose WND-Identifier, a universal and extensible framework for the identification of wireless devices, which can generate high-precision device labels (vendor, type, and product model) efficiently without user interaction. We first introduce the concept of device-info-related network protocols. WND-Identifier makes full use of the natural language features in such protocol messages and combines with the device description in the welcome page, thereby utilizing extraction rules to generate concrete device labels. Considering that the device information in the protocol messages may be incomplete or forged, we further take advantage of the application logic independence and stability of the device-info-related protocol, so as to build a multiprotocol text classification model, which maps the device to a known label. We conduct experiments in homes and public networks and present three application scenarios to verify the effectiveness of WND-Identifier.

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“…None of the developed methods considers privacy preservation on audio data. However, recently smart home IoT devices are increasing adding audio recording and monitoring features to a numerous household devices including smart speakers, virtual assistant AI devices, security systems, thermostats, doorbells, voice control devices (TV, vacuum cleaner), and many more [3]. All of the voice control devices are equipped with recorders and microphones which significantly increases privacy concerns.…”

Section: Introductionmentioning

confidence: 99%

Supporting Audio Privacy-aware Services in Emerging IoT Environment

Nower¹

2021

IJWMT

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The increasing exploration of smart space voice assistants and audio monitoring IoT devices leads to significant trust and privacy concerns. Among them, some of the data are sensitive and a user may not aware of when and where audio recordings are sent for processing. This provides burning questions-how do users gain knowledge of, and control the amount of data captured in the physical world. Moreover, the sending and processing of these sensitive data to the far cloud do not provide an efficient solution for sensitive data which causes serious privacy and security concerns. In this paper, a new architecture for providing audio transparency and privacy in the IoT-rich environment is proposed. The proposed privacy enforcement module is used to operate within a nearby fog node, which situated close to the data sources and enforces privacy according to the data owner's desire. To show the effectiveness of the proposed architecture, a well-known privacy threat framework is investigated.

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LeakyPick: IoT Audio Spy Detector

Cited by 28 publications

References 17 publications

MotionCompass

MotionCompass

WND-Identifier: Automated and Efficient Identification of Wireless Network Devices

Supporting Audio Privacy-aware Services in Emerging IoT Environment

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