IPv6Landmarker: Enhancing IPv6 Street-Level Geolocation Through Network Landmark Mining and Targeted Updates

Ruosi Cheng, Shichang Ding, Liancheng Zhang, Ruixiang Li, Shaoyong Du, Xiangyang Luo

Abstract: IP geolocation accuracy heavily relies on the availability of numerous high-quality network landmarks. However, IPv6 geolocation faces challenges due to its vast address space and rotating prefixes. Existing landmark mining methods struggle to meet the stringent demands of IPv6 street-level geolocation. We introduce IPv6Landmarker, a novel approach that enhances IPv6 geolocation precision through landmark mining and targeted updates. By associating WAN IPv6 addresses with WiFi BSSIDs in wireless routers, we employ a multi-association coordinate filtering algorithm to select reliable IPv6 street-level landmarks. We also implement targeted updates based on IPv6 prefix rotation patterns. Using real-world data, we demonstrate significant improvements, including a range increase of 16.75% to 46.68% in candidate landmarks acquired globally and of 10.06% to 126.39% in landmarks acquired specifically within target cities. In particular, there is a range of 16.67% to 66.67% enhancement in the geolocation success of ground truth landmarks, coupled with a range of 6.09% to 40.34% reduction in geolocation error. Additionally, it shows a remarkable 82.36% improvement in landmark set stability.