## 4 Results
### Experiments and Analysis
#### 4.2.2 Comparative Experiments
However, these two models have relatively more parameters and complex models, which  
Figure 15: Confusion matrix of the proposed network, existing external intrusion detection networks, and state-of-the-art deep learning networks. may lead to overfitting, and the inference speed is not fast enough to meet the requirements of lightweight and fast inference in practical applications. In contrast, TEResNet performs well in terms of accuracy and computational efficiency, with an accuracy of 97.12% and an F1 score of 96.15%, which is significantly better than the other models. In terms of inference time, LSTM and GRU are faster but have poor detection performance. Among the models with accuracy greater than 90%, TEResNet has the fastest inference speed of 0.0480 ms/sample, with only 48,009 trainable parameters, which achieves the improvement of accuracy and efficiency. This is due to the fact that it makes full use of the structural advantages of temporal convolution and residual networks, and is able to efficiently extract key features in the temporal signal with fewer layers. Precision and recall are also crucial metrics in intrusion detection, and the results in Table 4 show that the models with accuracy greater than 90% have 100% precision, which indicates that false positives occur less frequently, and that the system rarely generates false alarms; however, the recall can only reach a maximum of 92.59%, which indicates that there are more false negative cases, and the system misses a portion of the real attacks. Figure 17 shows the time series signals, their spectrograms, and the corresponding output probabilities for correctly and incorrectly classified abnormal samples. The correctly classified samples usually have significant feature patterns, which are manifested as regular \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy (\%)/Precision (\%)/Recall (\%)** & **F1 Score (\%)** & **Parameters** & **Inference Time (ms/Sample)** \\ \hline CNN & 75.54/65.63/77.78 & 71.19 & 395,681 & 0.0544 \\ LSTM & 76.50/64.95/85.80 & 73.94 & 3,289,601 & 0.0180 \\ GRU & 75.30/63.98/83.33 & 72.39 & 2,500,097 & **0.0095** \\ CNN-LSTM-AM & 86.57/**100**/65.43 & 79.10 & 4,316,293 & 1.1912 \\ Transformer & 88.73/89.12/80.86 & 84.79 & 530,434 & 24.30 \\
1 DResNet & 88.49/**100**/70.37 & 82.61 & 3,844,930 & 0.0800 \\ LCANet & 90.41/100/75.31 & 85.92 & 87,105 & 0.1269 \\ STFT+2 DResNet & 95.68/**100**/88.89 & 94.12 & 11,168,193 & 0.3196 \\ CLDNN & 96.40/**100**/90.74 & 95.15 & 4,742,081 & 0.0626 \\
**TEResNet (our)** & **97.12/100/92.59** & **96.15** & **48,009** & 0.0480 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of the proposed method with existing external intrusion detection methods and advanced deep learning networks, where the bolded results represent the best performance. Figure 16: Comparison of F1 scores and model parameters across different methods for external intrusion detection. and strong fluctuations in the time series signals, a concentrated distribution of energy in the spectrograms, and consistently high predicted probabilities, often reaching values above 0.9. In contrast, the misclassified samples lack clear features, as evidenced by irregular amplitude fluctuations in the time series signals, scattered energy distribution in the spectrograms, and very low predicted probabilities, typically below 0.1. These issues may arise from noise interference or signal attenuation, which are common challenges in anomalous intrusion detection for industrial environments. In addition to that, an important reason for this is that abnormal signals in intrusion detection tasks tend to be a minority class. When the real attack samples are insufficient or unevenly distributed, the model does not learn the minority class features sufficiently and is prone to miss reports.