基于横向联邦学习的风险用户识别研究  

Risk user identification based on horizontal federated learning

作　　者：樊春美 杨立鹏 李雯 张智 FAN Chunmei;YANG Lipeng;LI Wen;ZHANG Zhi(Institute of Computing Technologies,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China)

机构地区：[1]中国铁道科学研究院集团有限公司电子计算技术研究所,北京100081

出　　处：《铁路计算机应用》2024年第8期72-77,共6页Railway Computer Application

基　　金：中国国家铁路集团有限公司2023系统性重大项目(P2023W002);中国铁道科学研究院集团有限公司电子计算技术研究所项目(DZYF23-11)。

摘　　要：第三方平台推出的各种铁路旅客抢票服务,给中国铁路12306互联网售票系统(简称:12306)带来了较大压力,为保障12306的稳定性和旅客购票的公平性,亟需对风险用户进行识别。为应对因12306部署在不同的物理位置、不同中心的数据聚合存在一定风险的情况,研究在用户数据分散的条件下,基于横向联邦学习的风险用户识别方法。文章基于用户的访问行为,构建和提取用户特征,构建基于XGboost、逻辑回归和神经网络等算法的横向联邦学习模型,并进行模型验证。实验结果表明,基于XGboost算法的横向联邦学习模型具有较好的风险用户识别效果,为铁路数据的安全使用提供了技术支撑。Various railway passenger ticket grabbing services launched by the third-party platform have brought great pressure to the China railway 12306 Internet ticketing and reservation system(12306 for short).In order to ensure the stability of 12306 and the fairness of passenger ticket purchase,it is urgent to identify risk users.This paper aimed to address the risk of data aggregation caused by the deployment of 12306 in different physical locations and centers,studied a risk user identification method based on horizontal federated learning under the condition of dispersed user data.Based on user access behavior,the paper constructed and extracted user features,constructed a horizontal federated learning model using algorithms such as XGboost,logistic regression,and neural networks,and validated the model.The experimental results show that the horizontal federated learning model based on XGboost algorithm has good risk user recognition performance,provides technical support for the safe use of railway data.

关 键 词：12306互联网售票系统 横向联邦学习 XGBoost 风险用户 神经网络 

分 类 号：U293.221[交通运输工程—交通运输规划与管理] TP39[交通运输工程—道路与铁道工程]