Name
Abstract | ||
|---|---|---|
Recent research advancement of wireless sensing technology has made device-free interaction in the WiFi-enabled IoT environment possible. Although gesture-based interaction with such a smart environment greatly improves usability, it also introduces many security problems, such as shoulder surfing attacks. By spoofing the gestures of legitimate users, the attacker could easily access private information or services and cause even worse consequences. A secure interaction mechanism for this environment is required to prevent attackers without compromising the usability, while the limited recognition ability and low robustness of WiFi sensing make this target extremely challenging. To this end, we propose a secure interaction mechanism called secure interaction via WiFi Signal (SiWi), which provides the ability to resist shoulder surfing attacks without compromising the usability by using just WiFi signals. SiWi innovates in a concurrent interaction/authentication framework with only three elemental gestures (push, swing, and wave) and four types of identity-related imperceptible/hidden features (time distribution, direction, angle, and distance). HMM and Fresnel model-based algorithms are used to recognize the gestures and extract hidden features robustly and efficiently. Extensive experiments in a real implemented system were conducted to investigate the effectiveness of the proposed secure interaction system. The results show that our system can achieve an average accuracy of 93% to identify legitimate users and 97% to resist the spoofer. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/JIOT.2020.3032623 | IEEE Internet of Things Journal |
Keywords | DocType | Volume |
Channel state information (CSI),device-free sensing,gesture recognition,secure interaction,wireless sensing | Journal | 8 |
Issue | ISSN | Citations |
7 | 2327-4662 | 1 |
PageRank | References | Authors |
0.35 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanchao Zhao | 1 | 74 | 14.27 |
| Ran Gao | 2 | 1 | 1.02 |
| Shangqing Liu | 3 | 7 | 3.87 |
| Lei Xie | 4 | 1 | 1.70 |
| Jie Wu | 5 | 23 | 11.49 |
| Huawei Tu | 6 | 1 | 3.39 |
| Bing Chen | 7 | 31 | 11.00 |