| Abstract |
Wi-Fi plays an essential role in various emerging Internet of Things (IoT) services and applications in smart cities and communities, such as IoT access, data transmission, and intelligent control. However, the openness of such wireless communication medium makes IoT extremely vulnerable to conventional Wi-Fi attacks, of which one is rouge access point (RAP) attacks. This attack brings about serious privacy leakage and property damage to IoT users, motivating in-depth research on RAP attack detection in both academic and industrial communities. Recently, the phase error extracted from channel state information (CSI) has been extensively explored as a physical-layer hardware fingerprint to realize RAP detection. However, in this article, we discover that the phase error suffers from an fingerprint fracture phenomenon (FFP), leading to the complete failure of environment noise filters applied in state-of-the-art approaches and resulting in unsatisfactory detection accuracy. Inspired by our significant discovery, we propose a deep-learning RAP detection method named DL-PEDR. It innovatively offers an Auto-NRK network to effectively remove the environment interference on phase error drift range and inputs it into a Self-ACC network as a reconstructed device fingerprint to accurately authenticate the access point (AP) identity. Through comprehensive evaluation experiments with 30 commonly used Wi-Fi routers, we demonstrate that DL-PEDR achieves a 100% device distinction rate and a 96.6% RAP detection rate under dynamic environments. Moreover, we collect and share more than 1.5 million pieces of CSI data to alleviate the need for large-scale public CSI data sets. © 2014 IEEE. |
