Abstract: IP geolocation,as a fundamental technology for cyberspace mapping and management,has significant applications in various domains such as network security,content recommendation,and financial risk control.With the rapid development of artificial intelligence,neural networks have become the predominant modeling paradigm for IP geolocation in recent years,with most existing studies focusing on minimizing the average geolocation error.However,in risk-sensitive scenarios,the controllability of localization errors is equally critical,and modeling paradigms that solely focus on minimizing average errors often fall short of practical requirements.To address this issue,this paper proposes a trustworthy IP geolocation method that integrates graph neural networks with conformalized quantile regression.Unlike traditional point estimation methods,the proposed method produces prediction intervals with guaranteed confidence levels,enabling verifiable control over the range of geolocation errors.Experimental results on multiple real-world datasets demonstrate that the proposed method achieves a prediction interval coverage deviation of less than 1% at the 90% confidence level,while maintaining narrow interval widths,significantly enhancing the trustworthiness and practicality of IP geolocation in risk-sensitive scenarios.

Key words: IP geolocation, Graph neural networks, Trustworthy, Conformal prediction, Quantile regression