基于随机森林的IP地址城市级定位方法研究

Research on IP City-Level Geolocation Based on Random Forest

针对传统IP地址定位方法准确度低的问题,提出了一种基于随机森林的IP地址城市级定位方法。该方法分析了IP自身特点及IP间存在关系,分别对经过路由特征和地域触发特征进行了定义,并通过主动测量提取IP多维度特征,然后结合机器学习的思想训练构建分类器,以实现对IP所处城市的定位。在此基础上,利用真实的IP地址数据集对所提定位方法进行实证分析。实验结果表明,基于随机森林的IP地址城市级定位方法可以实现IP在物理空间上到城市级的精准定位,且定位结果与主流IP定位工具一致。此外,该方法模型训练时间短且无需进行复杂调优,在保证定位可信度的前提下运行耗时少,具有更高的实际应用价值。

Aiming at the low accuracy of traditional IP address location method,this paper proposes a IP city-level geoloca⁃tion method based on random forest.This method analyzes the characteristics of IP itself and the relationship between IPs,and de⁃fines the routing characteristics and geographical triggering characteristics respectively.Then,the IP multi-dimensional feature is extracted by active measurement,and the classifier is constructed in combination with the idea of machine learning to realize the po⁃sitioning of the city where the IP is located.On this basis,the proposed localization method is empirically analyzed using real IP ad⁃dress data sets.Experimental results show that the IP city-level geolocation method based on random forest can achieve accurate IP geolocation to city level,and the positioning results are highly consistent with the mainstream IP positioning tools.In addition,the model training time of this method is short and no complex tuning is required,so it takes less time to run under the premise of ensur⁃ing the credibility of positioning and has higher practical application value.