【摘要】 GNSS已成为电力、通信、金融等基础设施获取高精度授时服务的核心手段，其安全性直接关系基础设施稳定运行能力.本文针对GNSS授时设备易被欺骗的问题，设计了一种基于随机森林的授时欺骗检测方法.该方法充分利用GNSS授时设备往往固定安装，其信号接收环境变化缓慢、信号较为稳定的特点，构建基于核函数思想的高阶判别特征，并通过随机森林(random forest,RF)模型增强特征协同性，提升欺骗检测能力.北斗卫星导航系统(BeiDou Navigation Satellite System,BDS) B1I、B3I实采数据测试表明，当决策树数量为30时，模型袋外(out-of-bag,OOB)误差约为0.13%，在独立测试集及另一验证场景下综合评估精确率(Precision)和召回率(Recall)的F1分数优于99%，这充分验证了本方法具备优秀的欺骗检测能力与跨场景迁移潜力，能够有效提升GNSS授时设备的安全性.更多还原

【Abstract】 GNSS has been widely adopted as a primary source of high-precision timing for critical infrastructures such as power grid, communications and financial systems. Its security is therefore essential to the reliable operation of these services. To address the vulnerability of GNSS timing equipment to spoofing attacks, this paper designs a random forest based spoofing detection method for GNSS timing. By leveraging the consistency in signal statistical characteristics arising from the periodic nature of GNSS satellite orbits,the method constructs high discriminative features inspired by kernel function concepts and enhance feature cooperativity through a random forest model, thereby improving detection performance. Tests with real collected data on BeiDou Navigation Satellite System(BDS) B1I and B3I signals show that with 30 decision trees, the model’s out-of-bag(OOB) error is approximately 0.13%. The F1-score, which comprehensively balances precision and recall, exceeds 99% on both the independent test set and an additional validation scenario. These results fully validate the method’s high accuracy, strong robustness and good generalization capability, confirming its effectiveness in enhancing the security of GNSS timing equipment.更多还原