## 5 Conclusions  
In this paper, we propose a novel network, TEResNet, that enables robust and lightweight external intrusion detection in metro tunnels. To address the challenges of efficiently detecting anomalous signals, TEResNet combines temporal convolutions with a compact residual network architecture. Unlike traditional two-dimensional convolutional methods, which require deep architectures to capture both low- and high-frequency domains, it integrates lower-level features into the generation of higher-level features, thereby reducing the number of layers while still maintaining a superior performance. This design not only enhances robustness, but also significantly reduces the size of the feature map, effectively lowering the computational burden and enabling rapid detection. To evaluate the method, we conducted experiments in the construction environment of the Guangzhou Metro and created a dataset for external intrusion detection. After preprocessing the signals  
Figure 19: Real-time output probabilities and raw signal amplitude visualization of the external intrusion detection model under more difficult conditions, where the red dots represent the time corresponding to the percussive tap.  
collected by fiber optic sensors, STFT features are extracted as inputs to the neural network. Experimental results validate the performance of TEResNet, achieving an accuracy of 97.12% and an F1 score of 96.15%. With only 48,009 learnable parameters, it demonstrates the effectiveness and reliability of anomalous detection while offering a lightweight and efficient solution. It has practical implications for real-time intrusion detection in structures such as metros, train tracks, and bridges, enabling the monitoring of the operation of such infrastructures with low resource consumption.  
Since external intrusions are simulated by tapping, the dataset is limited in diversity, making it difficult to comprehensively assess the performance under real-world conditions. Future work will expand the dataset by considering more intrusion scenarios and environmental conditions to ensure that the system is able to handle situations that have never been seen or simulated before. In addition, practical factors, such as the optimal placement of sensors, integration of the system into complex metro tunnel environments, and regular maintenance schedules, will all have an impact on the performance of the method, and we will add a simulation analysis and field trials to refine these aspects.  
Conceptualization, Y.W. and R.Z.; methodology, Y.W., Z.G. and R.Z.; software, H.L.; validation, Y.W., H.L. and Z.G.; formal analysis, H.L.; investigation, J.L.; data curation, Y.W., H.L. and J.L.; writing--original draft preparation, Y.W.; writing--review and editing, Z.G. and R.Z.; supervision, J.L.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key Research and Development Program of China under Grant No. 2022 YFC3005200.  
Not applicable.  
Data Availability Statement: Data are available at [[https://github.com/guozive97/Metro-Tunnel-Anomaly-Detection-Data](https://github.com/guozive97/Metro-Tunnel-Anomaly-Detection-Data)]([https://github.com/guozive97/Metro-Tunnel-Anomaly-Detection-Data](https://github.com/guozive97/Metro-Tunnel-Anomaly-Detection-Data)) (accessed on 20 January 2025).  
The authors declare no conflicts of interest.