Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/INFOCOM42981.2021.9488866 | IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (IEEE INFOCOM 2021) |
DocType | ISSN | Citations |
Conference | 0743-166X | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanzhi Ren | 1 | 8 | 2.39 |
| Ping Wen | 2 | 0 | 0.34 |
| Hongbo Liu | 3 | 12 | 2.88 |
| Zhourong Zheng | 4 | 0 | 0.34 |
| Yingying Chen | 5 | 2495 | 193.14 |
| Pengcheng Huang | 6 | 0 | 0.34 |
| Hongwei Li | 7 | 154 | 12.60 |