Sound-based proximity detection with mobile phones

Thiel, Benjamin; Kloch, Kamil; Lukowicz, Paul

doi:10.1145/2389148.2389152

Cited by 19 publications

(13 citation statements)

References 7 publications

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“…Halevi et al proposed a method that can detect proximity using ambient sound in NFC payment systems [33]. Thiel et al proposed a proximity-detection method with mobile phones that is based on sound beacons in an inaudible spectrum around 18 KHz [44]. Even though both the above methods are similar to ours, their method cannot be easily applied to PKES systems.…”

Section: Received Signal Strength Indicator (Rssi)mentioning

confidence: 97%

Sound-Proximity: 2-Factor Authentication against Relay Attack on Passive Keyless Entry and Start System

Choi

Seo

Lee

2018

Journal of Advanced Transportation

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Passive keyless entry and start system has been widely used in modern cars. Car owners can open the door or start the engine merely by having the key in their pocket. PKES was originally designed to establish a communication channel between the car and its key within approximately one meter. However, the channel is vulnerable to relay attacks by which attackers unlock the door even if the key is out of range. Even though relay attacks have been recognized as a potential threat for over ten years, such attacks were thought to be impractical due to highly expensive equipment; however, the required cost is gradually practical. Recently, a relay attack has been demonstrated with equipment being sold only under $100. In this paper, we propose a sound-based proximity-detection method to prevent relay attacks on PKES systems. The sound is eligible to be applied to PKES because audio systems are commonly available in cars. We evaluate our method, considering environments where cars are commonly parked, and present the recording time satisfying both usability and security. In addition, we newly define an advanced attack, called the record-and-playback attack, for sound-based proximity detection, demonstrating that our method is robust to such an attack.

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Section: Received Signal Strength Indicator (Rssi)mentioning

confidence: 97%

Sound-Proximity: 2-Factor Authentication against Relay Attack on Passive Keyless Entry and Start System

Choi

Seo

Lee

2018

Journal of Advanced Transportation

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“…42 It is also possible to capture the user's ambient sound through their mobile phone and detect the presence, location and activity. The proximity of an occupant is detected with the help of an ambient sound signal received by their mobile phone, 91 and the occupant's activity is estimated. This can further be used to identify the energy demand of the user without any additional sensing infrastructure.…”

Section: Alternate Occupancy Sensing Approachesmentioning

confidence: 99%

Occupancy detection systems for indoor environments: A survey of approaches and methods

Trivedi

Badarla

2019

Indoor and Built Environment

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Utilization of smart services for indoor environments has gained extensive interest in the past decade due to the resulting increases in energy savings, user comfort and degree of automation. Occupancy detection is a critical element in automation systems due to its potential use in controlling electrical systems and devices such as lighting, air-conditioning and ventilation. It also has a high potential for improving the performance of demand-driven applications which require fine-grained occupancy information to optimize the trade-off between energy consumption and user comfort. Occupancy detection has been researched using different estimation methods and communication technologies. However, it remains challenging to procure sensory data and to model the occupancy information accurately due to the limitations of hardware deployment and underlying cost. This paper reviews existing occupancy methods and applications, along with their underlying issues. It provides a comparative analysis of the strategies from the perspectives of cost, intrusiveness and accuracy. Additionally, a new taxonomy which classifies the occupancy sensing techniques as being conventional or as alternate sensing has been proposed.

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“…Ultrasonic tones have been used for data transfer [1] and proximity detection [31] as well; however, these have never been used for relative arrangement detection. We use an 18 kHz sine wave for the left speaker and 18.5 kHz for the right speaker.…”

Section: Detecting Device Arrangementmentioning

confidence: 99%

SurfaceLink

Goel

Lee

Aumi

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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We present SurfaceLink, a system where users can make natural surface gestures to control association and information transfer among a set of devices placed on a mutually shared surface (e.g., a table). SurfaceLink uses a combination of on-device accelerometers, vibration motors, speakers, and microphones (and, optionally, an off-device contact microphone for greater sensitivity) to sense gestures performed on the shared surface. In a controlled evaluation with 10 participants, SurfaceLink detected the presence of devices on the same surface with 97.7% accuracy, their relative arrangement with 89.4% accuracy, and a set of single-and multi-touch surface gestures with an average accuracy of 90.3%. A usability analysis showed that SurfaceLink has advantages over current multi-device interaction techniques in a number of situations.

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Sound-based proximity detection with mobile phones

Cited by 19 publications

References 7 publications

Sound-Proximity: 2-Factor Authentication against Relay Attack on Passive Keyless Entry and Start System

Sound-Proximity: 2-Factor Authentication against Relay Attack on Passive Keyless Entry and Start System

Occupancy detection systems for indoor environments: A survey of approaches and methods

SurfaceLink

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