Detecting Evil-Twin Attack with the Crowd Sensing of Landmark in Physical Layer

With the popularity of mobile computing, WiFi has become one of the essential technologies for people to access the Internet, and WiFi security has also become a major threat for mobile computing. The Evil-Twin attack can steal a large amount of private data by forging the same SSID as the real Access Point. This paper proposes a passive Evil-Twin attack detection scheme through CSI in physical layer. First of all, we propose a location model based on the edge of landmark area. In this model, the improved MUSIC algorithm is used to calculate each AP’s AoA by CSI phase. Secondly, it proposes an algorithm for simplifying the generation of location model files, which is the dataset of a small number of AoA and RSSI samples. Finally, according to location model, attack detection algorithm combines a large number of crowd sensing data to determine whether it is a malicious AP. Experiments show that our attack detection system achieves a higher detection rate.