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 indoor location awareness has drawn increasing attention as the mobile apps are used extensively in our daily lives. Existing indoor localization solutions either require a pre-installed infrastructure or can only achieve room-level accuracy, which could not provide a function-location service for mobile devices. In this work, we propose a new active sensing system that enables smartphones to identify some pre-defined indoor locations robustly without requiring any additional sensors or pre-installed infrastructure. The main idea behind our system is to utilize the acoustic signatures, which are derived from the mobile device by emitting a beep signal and selecting its echoes created by dominant reflectors, as the robust fingerprint for location identification. Given the microphone samplings, our system designs a correlation based technique to accurately detect the beginning points of echoes from the received beep signal. To achieve a robust location identification, we develop a new echo selection scheme to select echoes created by dominant reflectors by exploiting the relationships between propagation delays of different orders of echoes. To deal with the variable number of selected echoes, our location identification component then derives histograms from selected echoes and uses the one-against-all SVM classifiers to determine the current location. Our experimental results show that our proposed system is accurate and robust for location identification under various real-world scenarios.

The location awareness becomes increasingly important as mobile devices such as smartphones are used extensively in our daily lives. Existing indoor localization solutions either require certain preinstalled infrastructures or add-on devices, which could not provide a location semantics identification service for smartphones to infer both type and size of a geographic location. In this work, we propose a new active sensing system that enables smartphones to identify its location semantics without requiring any additional infrastructure. The main idea behind our system is to utilize the acoustic signatures, which are derived from the smartphone by emitting a predesigned beep signal and identifying two echo sets which correspond to sidewalls and other static objects respectively, as the proof to achieve both spatial size estimation and room-type prediction simultaneously for indoor location semantics identification. Given the microphone samplings, our system designs a correlation-based scheme to identify beginning points of echoes corresponding to static reflectors accurately from the received signal. To achieve an accurate location semantics identification, we develop a new echo selection scheme to discriminate echoes created by sidewalls and other static reflectors by utilizing the geometrical relationships between the delays of echoes. To deal with the varying number of identified echoes, our location semantics prediction scheme then derives histograms from echo sets and adopt a deep-learning-based classifier to determine the current location semantics. Our experimental results show that our proposed system is accurate and robust for location semantics identification under various real-world scenarios.