Sk. Asiff, R. Deepthi, Shaik Ishaq, Shaik Abdul Bhasha, Konisetty Manoj Kumar, Yakasiri Penchala Prasad 03 04(1) In modern railway networks realtime 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 rulebased approaches which are slow errorprone and inadequate for handling the dynamic and highvolume nature of modern rail communication data These limitations highlight the need for a robust automated anomaly detection framework capable of accurate realtime 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 SentenceBERT 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 F1score confusion matrix and ROC curves demonstrates that the proposed method significantly improves anomaly detection while reducing false positives and supporting reliable realtime rail communication monitoring Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026