Abstract
WiFi positioning systems (WPS) have been introduced as parts of 5G location services (LCS) to provide fast positioning results of user devices in urban areas. However, they are prominently threatened by location spoofing attacks. To end this, we present a Wasserstein metric-based attack detection scheme to counter the location spoofing attacks in the WPS. The Wasserstein metric is used to measure the similarity of each two hotspots by their signal’s frequency offset distribution features. Then, we apply the clustering method to find the fake hotspots which are generated by the same device. When applied with WPS, the proposed method can prevent location spoofing by filtering out the fake hotspots set by attackers. We set up experimental tests by commercial WiFi devices, which show that our method can detect fake devices with 99% accuracy. Finally, the real-world test shows our method can effectively secure the positioning results against location spoofing attacks.
Highlights
Driven by the demands of location-based services (LBS) and the Internet of ings (IoT), 3GPP Release 16 has introduced a variety of positioning technologies as supplements to the cellular-based positioning method in the 5G location services (LCS) [1]
In each round of tests, we first get the confusion matrix of the test dataset as shown in Table 2, where TP is true positive, which refers to the number of correctly detected fake WiFi access points (AP). e FN means false negative, and TN/ FP stands for the counts of true negative and false positive
Unlike the channel status information (CSI) and received signal strength indication (RSSI)-based methods, our attack detection method is independent of the communication channel parameters, so it will keep stable performance despite the change in the location of devices; secondly, this is a user-side detection method, which does not require additional communication overhead and collaborative devices. ird, our method does not need to record and learn the feature of legitimate devices in advance, which is the biggest difference compared with most other RF fingerprint (RFF)-based methods
Summary
Driven by the demands of location-based services (LBS) and the Internet of ings (IoT), 3GPP Release 16 has introduced a variety of positioning technologies as supplements to the cellular-based positioning method in the 5G location services (LCS) [1]. The hybrid LCS architecture integrates global navigation satellite systems (GNSS) and WiFi positioning systems (WPS), to offer a positioning result of high accuracy, availability, and reliability. Applications such as autonomous driving, unmanned aerial vehicles, and massive IoT tracking will benefit from the improvement of LCS. In the architecture of LCS, GNSS can provide the most accurate position for the user’s mobile device in the open area, but it suffers from the poor visibility of satellites in the urban area and high power consumption [2]. Due to the opening nature of WiFi technology, the WPS is the weakest part of LCS in the cases of both trusted and untrusted access
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