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

Kai Yang, Jiayang Li, Wenxin Tai, Zhenhui Li, Ting Zhong, Guangqiang Yin, Yong Wang

AAAI 2024 pp. 23693-23695

doi:10.1609/AAAI.V38I21.30529 /aaai/2024/yang2024aaai-improving/

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.

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Cite

Text

Yang et al. "Improving IP Geolocation with Target-Centric IP Graph (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30529

Markdown

[Yang et al. "Improving IP Geolocation with Target-Centric IP Graph (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/yang2024aaai-improving/) doi:10.1609/AAAI.V38I21.30529

BibTeX

@inproceedings{yang2024aaai-improving,
  title     = {{Improving IP Geolocation with Target-Centric IP Graph (Student Abstract)}},
  author    = {Yang, Kai and Li, Jiayang and Tai, Wenxin and Li, Zhenhui and Zhong, Ting and Yin, Guangqiang and Wang, Yong},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {23693-23695},
  doi       = {10.1609/AAAI.V38I21.30529},
  url       = {https://mlanthology.org/aaai/2024/yang2024aaai-improving/}
}