用流程挖掘实时监控列车控制系统，发现并定位运行异常。
Run-Time Monitoring of ERTMS/ETCS Control Flow by Process Mining
- 从系统执行日志中学习真实控制流，实现在线合规性检查。
- 在RBC切换场景中检测异常准确率高，定位效率快。
- 适合铁路安全团队和系统运维人员，提升系统韧性。
保障基于计算机的铁路系统韧性对应对日益复杂的系统不确定性与变化至关重要。尽管其软件遵循严格的验证与确认流程及认证标准，但运行时仍可能因残留缺陷、设计时未知的系统或环境变更，以及新兴网络威胁导致异常。本文探索利用流程挖掘技术实现ERTMS/ETCS L2（欧洲铁路交通管理系统/列车控制系统等级2）的运行时控制流异常检测，以增强系统韧性。流程挖掘可从执行轨迹中学习系统实际控制流，从而通过在线合规性检查实现运行时监控。同时，采用无监督机器学习进行异常定位，将偏差关联至关键系统组件。我们在典型的ERTMS/ETCS L2场景——RBC/RBC切换中测试该方法，结果表明其具备高准确性、高效性和可解释性，能有效检测并定位异常。
原文摘要 · Abstract (English)
Ensuring the resilience of computer-based railways is increasingly crucial to account for uncertainties and changes due to the growing complexity and criticality of those systems. Although their software relies on strict verification and validation processes following well-established best-practices and certification standards, anomalies can still occur at run-time due to residual faults, system and environmental modifications that were unknown at design-time, or other emergent cyber-threat scenarios. This paper explores run-time control-flow anomaly detection using process mining to enhance the resilience of ERTMS/ETCS L2 (European Rail Traffic Management System / European Train Control System Level 2). Process mining allows learning the actual control flow of the system from its execution traces, thus enabling run-time monitoring through online conformance checking. In addition, anomaly localization is performed through unsupervised machine learning to link relevant deviations to critical system components. We test our approach on a reference ERTMS/ETCS L2 scenario, namely the RBC/RBC Handover, to show its capability to detect and localize anomalies with high accuracy, efficiency, and explainability.
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