The two-factor authentication ($2$FA) has drawn increasingly attention as the mobile devices become more prevalent. For example, the user's possession of the enrolled phone could be used by the$2$FA system as the second proof to protect his/her online accounts. Existing$2$FA solutions mainly require some form of user-device interaction, which may severely affect user experience and creates extra burdens to users. In this work, we propose a secure$2$FA system utilizing the proximity of a user's enrolled phone and the login device as the second proof without requiring the user's interactions. The basic idea of our$2$FA system is to derive location signatures based on acoustic beep signals emitted alternately by both devices and sensing the echoes with microphones, and compare the extracted signatures for proximity detection. Moreover, to further enhance the security of our system, we also design a device authentication scheme which derives the acoustic fingerprint between the login device and enrolled phone to verify the identity of two devices. Given the received beep signal, our system designs a period selection scheme to identify two sound segments accurately: the chirp period is the sound segment propagating directly from the speaker to the microphone whereas the echo period is the sound segment reflected back by surrounding objects. To achieve an accurate proximity detection, we develop a new energy loss compensation extraction scheme by utilizing the extracted chirp periods to estimate the intrinsic differences of energy loss between microphones of the enrolled phone and the login device. Our proximity detection component then conducts the similarity comparison between the identified two echo periods after the energy loss compensation to effectively determine whether the enrolled phone and the login device are in proximity for$2$FA. Moreover, to provide higher security, our device fingerprint-assisted proximity detection further utilizes the overall energy loss between the login device and enrolled phone as their hardware fingerprint to authenticate the identity of two devices. Our experimental results show that our system is accurate in providing$2$FA and robust to both man-in-the-middle (MiM) and co-located attacks across different scenarios and device models.

The increasingly popular usage of wearable devices provides users with the ability to continuously track their health conditions or physical activities. Such system is however vulnerable to user spoofing, in which a user distributes his/her device to other users such that the data collected from these users could be claimed to be his/her own. Thus, it is critical to identify the user for many wearable devices, allowing the sensing data to be labeled properly. In this paper, we propose a user identification system by leveraging the users whispered sound to mitigate user spoofing for wearable devices. Our system exploits the contact microphone placed into contact with the body to capture the users whispered sound for user identification. Given the captured acoustic data, our system first identifies frames which contain whispered events. Our system then calculates acoustic features from the identified whispered frames to determine whether the voice is collected when the microphone is on the body. Moreover, to make our system robust, we assign different quality weights to the whispers phonemes by considering their consistency (i.e., intra users differences) and distinctiveness (i.e., inter users differences) simultaneously. Our experiments demonstrate that our system is robust and accurate across various scenarios.

Exercise intensity monitoring of physical activities has drawn increasingly attention as the awareness of the exercise intensity is of great importance for a person to achieve optimal training outcomes. For example, over-training could lead to excessive fatigue and loss of motivation for exercise. Traditional exercise intensity monitoring systems utilize GPS data to track the user’s intensity of cardio activities through his/her position and speed. Such systems however become invalid for indoor exercises on stationary fitness equipments such as the treadmill or exercise bike. Recent work in using body-worn sensors to track the user’s heart rate for exercise intensity monitoring usually involves additional wearable sensors which are only available on some particular fitness equipments, and thus are hard to be used in all occasions. This work presents an exercise intensity monitoring system which is capable of detecting a person’s exercise intensity via smartphones. Our system exploits the off-the-shelf smartphone and its headphone to capture the user’s breathing sound. Given the captured acoustic data, our system performs data pre-processing to remove the environmental noise and identify the non-silent acoustic frames based on the signal energy. Our system then conducts breathing event detection for non-silent frames, and further calibrates the detection results by utilizing the high correlation between breathing cycles to improve the detection accuracy. Moreover, our system can estimate the person’s exercise intensity based on features extracted from the frames which contain breathing sound. Our experiments involving 9 subjects over four-month time period demonstrate that our proposed exercise intensity monitoring system is robust and accurate in both indoor and outdoor environments.

The two-factor authentication (2FA) has drawn increasingly attention as the mobile devices become more prevalent. For example, the user's possession of the enrolled phone could be used by the 2FA system as the second proof to protect his/her online accounts. Existing 2FA solutions mainly require some form of user-device interaction, which may severely affect user experience and creates extra burdens to users. In this work, we propose Proximity-Echo, a secure 2FA system utilizing the proximity of a user's enrolled phone and the login device as the second proof without requiring the user's interactions or pre-constructed device fingerprints. The basic idea of Proximity-Echo is to derive location signatures based on acoustic beep signals emitted alternately by both devices and sensing the echoes with microphones, and compare the extracted signatures for proximity detection. Given the received beep signal, our system designs a period selection scheme to identify two sound segments accurately: the chirp period is the sound segment propagating directly from the speaker to the microphone whereas the echo period is the sound segment reflected back by surrounding objects. To achieve an accurate proximity detection, we develop a new energy loss compensation extraction scheme by utilizing the extracted chirp periods to estimate the intrinsic differences of energy loss between microphones of the enrolled phone and the login device. Our proximity detection component then conducts the similarity comparison between the identified two echo periods after the energy loss compensation to effectively determine whether the enrolled phone and the login device are in proximity for 2FA. Our experimental results show that our Proximity-Echo is accurate in providing 2FA and robust to both man-in-the-middle (MiM) and co-located attacks across different scenarios and device models.