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机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出IPGeoAI模型，将IP地理定位转化为序列建模任务，融合Transformer与LLM语义特征，在城市级IP地理定位任务中实现准确率提升，覆盖全部流量并优化下游指标。
AI 中文摘要
准确的城市级IP地理定位是现代数字生态系统的重要支撑，为从本地内容分发与定向到数字权利执法等各类服务提供基础。然而，传统的启发式方法和数据库驱动方法往往难以解析现代网络基础设施复杂的非线性分配模式，尤其是在快速扩张的IPv6地址空间和瞬态移动网络中。本文提出IPGeoAI，一种新型深度学习模型架构，将地理定位从静态查找问题重新定义为序列建模任务。该方法利用Transformer编码器捕获IP子网结构中固有的层次依赖关系；通过零样本LLM特征提取流水线整合非结构化语义上下文，解决地理歧义问题。我们采用离线预计算流程，利用大型语言模型（LLM）将原始、噪声大的自治系统（AS）描述转换为结构化的领域特定元数据，例如“大学”与“互联网服务提供商（ISP）”、“全球”与“本地”等类别。通过多头交叉注意力模块将这些语义信号融合到网络中，弥合数值网络拓扑与现实世界语义标识之间的差距。对覆盖20万座城市的专有数据集进行的大量离线评估表明，IPGeoAI在城市级粒度上显著优于领先的外部供应商。通过采用细化粗粒度国家信号的层次推理策略，我们的模型实现了城市级准确率6%的提升，同时将覆盖范围扩展至100%的流量。此外，在大规模在线生产测试中，该模型推动我们的一级下游用例指标取得了具有统计显著性的+0.35%的提升。
英文摘要
Accurate city-level IP Geolocation is an important enabler for the modern digital ecosystem, underpinning services ranging from local content delivery and targeting to digital rights enforcement. However, traditional heuristic and database-driven methods often struggle to resolve the complex, non-linear allocation patterns of modern network infrastructures, particularly within the exploding IPv6 address space and transient mobile networks. In this paper, we introduce IPGeoAI, a novel deep learning model architecture that reframes geolocation from a static lookup problem to a sequential modeling task. Our approach utilizes the Transformer Encoder to capture hierarchical dependencies inherent in IP subnet structures. We propose a method to resolve geographic ambiguity by integrating unstructured semantic context via a Zero-Shot LLM Feature Extraction pipeline. We utilize Large Language Models to transform raw, noisy Autonomous Systems (AS) descriptions into structured, domain-specific metadata (such as 'University' vs. 'ISP' or 'Global' vs. 'Local') via an offline pre-computation process. By fusing these semantic signals into the network via a Multi-Head Cross-Attention module, we bridge the gap between numerical network topology and real-world semantic identity. Extensive offline evaluation on a proprietary dataset spanning 200,000 cities demonstrates that IPGeoAI significantly outperforms a leading external vendor in city-level granularity. By adopting a hierarchical inference strategy that refines coarse-grained country signals, our model achieves a 6% improvement in city-level accuracy while extending coverage to 100% of the traffic. Furthermore, in large-scale online production tests, the model drove a statistically significant +0.35% improvement in our 1st-tier downstream use cases metric.
