Improving IP Geolocation With Target-Centric IP Graph (Student Abstract)

Accurate IP geolocation is indispensable for location-aware applications. While recent advances based on router-centric IP graphs are considered cutting-edge, one challenge remain: the prevalence of sparse IP graphs (14.24% with fewer than 10 nodes, 9.73% isolated) limits graph learning. To mitigate this issue, we designate the target host as the central node and aggregate multiple last-hop routers to construct the target-centric IP graph, instead of relying solely on the router with the smallest last-hop latency as in previous works. Experiments on three real-world datasets show that our method significantly improves the geolocation accuracy compared to existing baselines.

Authors

• Kai Yang School of Computer Science and Technology, Tongji University, China
• Jiayang Li University of Electronic Science and Technology of China
• Wenxin Tai University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China Kash Institute of Electronics and Information Industry, Kashgar 844000, China
• Zhenhui Li University of Electronic Science and Technology of China
• Ting Zhong University of Electronic Science and Technology of China
• Guangqiang Yin University of Electronic Science and Technology of China Kashi Institute of Electronics and Information Industry
• Yong Wang

Keywords

• Data Mining
• Deep Learning
• Knowledge Discovery
• Knowledge Representation