Article
Context-Aware Rule Ensemble Learning for Adaptive Threat Detection in Intelligent Railway Signaling Data Streams
In modern railway networks, real-time monitoring and secure control communications are essential for ensuring operational safety, efficiency, and reliability. Critical operations such as signal control, train scheduling, and automated track switching generate large volumes of data that must be analyzed instantly to prevent failures or detect malicious intrusions. Traditional systems rely on manual inspections or rule-based approaches, which are slow, error-prone, and inadequate for handling the dynamic and high-volume nature of modern rail communication data. These limitations highlight the need for a robust, automated anomaly detection framework capable of accurate real-time classification. Existing methods, including Decision Tree with Cost Complexity Pruning (DTCCP) and Deep Neural Decision Tree (DNDT), provide interpretable models with moderate predictive performance. However, DTCCP often suffers from overfitting when dealing with complex sequential data, while DNDT may struggle to capture subtle contextual relationships, resulting in missed anomalies or false alarms. To address these issues, this research proposes a RuleFit (RF) classifier that combines linear rules with decision tree logic and semantic embeddings derived from Sentence-BERT (SBERT). This hybrid approach enables the system to learn both hierarchical decision boundaries and contextual patterns effectively. Performance evaluation using metrics such as accuracy, precision, recall, F1-score, confusion matrix, and ROC curves demonstrates that the proposed method significantly improves anomaly detection while reducing false positives and supporting reliable, real-time rail communication monitoring
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