【摘要】 文章提出一种基于集成学习的列车控制系统入侵检测方法，采用随机森林分类器实现一维多尺度卷积网络空间特征提取模型与自适应时间卷积网络时间特征提取模型的融合，降低网络泛化误差，提高入侵检测准确率。基于列车控制系统半实物仿真平台模拟的入侵检测数据集，文章对该方法进行实验评估和对比测试，结果证明，该方法更具优势。更多还原

【Abstract】 This paper proposes an intrusion detection method based on ensemble learning. The random forest classifier is used to integrate the spatial feature extraction model of one-dimensional multi-scale convolution network and the temporal feature extraction model of adaptive time convolution network, so as to reduce the network generalization error and improve the accuracy of intrusion detection. Based on the intrusion detection data set simulated by the hardware-in-the-loop simulation platform of train control system, this paper conducts experimental evaluation and comparative tests on the proposed intrusion detection method, and the results prove the advantages of the method.更多还原