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

Authors

  • Kai Yang University of Electronic Science and Technology of China
  • Jiayang Li University of Electronic Science and Technology of China
  • Wenxin Tai University of Electronic Science and Technology of China Kashi Institute of Electronics and Information Industry
  • Zhenhui Li University of Electronic Science and Technology of China
  • Ting Zhong University of Electronic Science and Technology of China Kashi Institute of Electronics and Information Industry
  • Guangqiang Yin University of Electronic Science and Technology of China Kashi Institute of Electronics and Information Industry
  • Yong Wang Hong Kong University of Science and Technology

DOI:

https://doi.org/10.1609/aaai.v38i21.30529

Keywords:

Data Mining, Knowledge Discovery, Knowledge Representation, Deep Learning

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.

Downloads

Published

2024-03-24

How to Cite

Yang, K., Li, J., Tai, W., Li, Z., Zhong, T., Yin, G., & Wang, Y. (2024). Improving IP Geolocation With Target-Centric IP Graph (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23693-23695. https://doi.org/10.1609/aaai.v38i21.30529