The increasingly complex and interconnected train control information network is vulnerable to a variety of malicious traffic attacks,and the existing malicious traffic detection methods mainly rely on machine learnin...The increasingly complex and interconnected train control information network is vulnerable to a variety of malicious traffic attacks,and the existing malicious traffic detection methods mainly rely on machine learning,such as poor robustness,weak generalization,and a lack of ability to learn common features.Therefore,this paper proposes a malicious traffic identification method based on stacked sparse denoising autoencoders combined with a regularized extreme learning machine through particle swarm optimization.Firstly,the simulation environment of the Chinese train control system-3,was constructed for data acquisition.Then Pearson coefficient and other methods are used for pre-processing,then a stacked sparse denoising autoencoder is used to achieve nonlinear dimensionality reduction of features,and finally regularization extreme learning machine optimized by particle swarm optimization is used to achieve classification.Experimental data show that the proposed method has good training performance,with an average accuracy of 97.57%and a false negative rate of 2.43%,which is better than other alternative methods.In addition,ablation experiments were performed to evaluate the contribution of each component,and the results showed that the combination of methods was superior to individual methods.To further evaluate the generalization ability of the model in different scenarios,publicly available data sets of industrial control system networks were used.The results show that the model has robust detection capability in various types of network attacks.展开更多
本文给出CTCS-3级列控系统中组件控制行为的形式化定义,并针对控制行为的时序关系,提出控制行为时序逻辑。以此时序逻辑为基础,给出控制关系模型的形式化定义,使用控制关系模型对列控系统中的控制行为关系进行刻画。利用深度优先搜索的...本文给出CTCS-3级列控系统中组件控制行为的形式化定义,并针对控制行为的时序关系,提出控制行为时序逻辑。以此时序逻辑为基础,给出控制关系模型的形式化定义,使用控制关系模型对列控系统中的控制行为关系进行刻画。利用深度优先搜索的方式,对系统的控制关系模型进行分析,实现STPA(System-Theoretic Process Analysis)过程中不恰当控制行为的自动化辨识。以CTCS-3级列控系统的RBC交接场景为例,使用上述基于控制关系模型的STPA方法对列控系统的功能安全进行分析。分析过程表明利用形式化的控制关系模型扩展STPA的方法适用于CTCS-3级列控系统的功能安全分析。展开更多
文摘The increasingly complex and interconnected train control information network is vulnerable to a variety of malicious traffic attacks,and the existing malicious traffic detection methods mainly rely on machine learning,such as poor robustness,weak generalization,and a lack of ability to learn common features.Therefore,this paper proposes a malicious traffic identification method based on stacked sparse denoising autoencoders combined with a regularized extreme learning machine through particle swarm optimization.Firstly,the simulation environment of the Chinese train control system-3,was constructed for data acquisition.Then Pearson coefficient and other methods are used for pre-processing,then a stacked sparse denoising autoencoder is used to achieve nonlinear dimensionality reduction of features,and finally regularization extreme learning machine optimized by particle swarm optimization is used to achieve classification.Experimental data show that the proposed method has good training performance,with an average accuracy of 97.57%and a false negative rate of 2.43%,which is better than other alternative methods.In addition,ablation experiments were performed to evaluate the contribution of each component,and the results showed that the combination of methods was superior to individual methods.To further evaluate the generalization ability of the model in different scenarios,publicly available data sets of industrial control system networks were used.The results show that the model has robust detection capability in various types of network attacks.
文摘本文给出CTCS-3级列控系统中组件控制行为的形式化定义,并针对控制行为的时序关系,提出控制行为时序逻辑。以此时序逻辑为基础,给出控制关系模型的形式化定义,使用控制关系模型对列控系统中的控制行为关系进行刻画。利用深度优先搜索的方式,对系统的控制关系模型进行分析,实现STPA(System-Theoretic Process Analysis)过程中不恰当控制行为的自动化辨识。以CTCS-3级列控系统的RBC交接场景为例,使用上述基于控制关系模型的STPA方法对列控系统的功能安全进行分析。分析过程表明利用形式化的控制关系模型扩展STPA的方法适用于CTCS-3级列控系统的功能安全分析。
