Improving IP Geolocation With Target-Centric IP Graph (Student Abstract)
Kai Yang, Jiayang Li, Wenxin Tai, Zhenhui Li, Ting Zhong, Guangqiang Yin, Yong Wang
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.
BibTeX
@article{Yang_Li_Tai_Li_Zhong_Yin_Wang_2024, title={Improving IP Geolocation With Target-Centric IP Graph (Student Abstract)}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30529}, DOI={10.1609/aaai.v38i21.30529}, abstractNote={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.}, number={21}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Yang, Kai and Li, Jiayang and Tai, Wenxin and Li, Zhenhui and Zhong, Ting and Yin, Guangqiang and Wang, Yong}, year={2024}, month={Mar.}, pages={23693-23695} }