TrustGeo: Uncertainty-Aware Dynamic Graph Learning for Trustworthy IP Geolocation

Author:

Tai Wenxin1ORCID,Chen Bin1ORCID,Zhou Fan1ORCID,Zhong Ting1ORCID,Trajcevski Goce2ORCID,Wang Yong3ORCID,Chen Kai3ORCID

Affiliation:

1. University of Electronic Science and Technology of China, Chengdu, China

2. Iowa State University, Ames, USA

3. Hong Kong University of Science and Technology, Hong Kong, China

Funder

Hong Kong RGC TRS

National Science Foundation SWIFT

Natural Science Foundation of Sichuan Province

National Natural Science Foundation of China

Publisher

ACM

Reference37 articles.

1. A review of uncertainty quantification in deep learning: Techniques, applications and challenges

2. Deep evidential regression;Amini Alexander;Advances in Neural Information Processing Systems,2020

3. Sercan Ö. Arik and Tomas Pfister . 2021 . TabNet: Attentive Interpretable Tabular Learning. In AAAI. 6679--6687. Sercan Ö. Arik and Tomas Pfister. 2021. TabNet: Attentive Interpretable Tabular Learning. In AAAI. 6679--6687.

4. Towards IP location estimation using the nearest common router;Luo Xiangyang;Journal of Internet Technology,2018

5. Ovidiu Dan Vaibhav Parikh and Brian D Davison. 2021. IP Geolocation Using Traceroute Location Propagation and IP Range Location Interpolation. In WWW. 332--338. Ovidiu Dan Vaibhav Parikh and Brian D Davison. 2021. IP Geolocation Using Traceroute Location Propagation and IP Range Location Interpolation. In WWW. 332--338.

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1. Exploring Self-Explainable Street-Level IP Geolocation with Graph Information Bottleneck;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

2. ProbeGeo: A Comprehensive Landmark Mining Framework Based on Web Content;IEEE/ACM Transactions on Networking;2024

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