# Adversarial Hierarchical-Aware Edge Attention Learning Method for Network Intrusion Detection
## 5 Experiments and Results
### Comparison with SOTA Methods
On the other hand, although AH-EAT's fine-grained accuracy (42.25%) lags behind E-GraphSAGE (49.36%), AH-EAT has two training targets, namely coarse-grained and fine-grained training targets, so it may not be as effective as focusing on only one training target (E-GraphSAGE). However, the difference between the two is not large, which is a limitation shared by all methods in this paper, but is more significant in IoT data due to its constrained feature space. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**UNSW-NB15**} & \multicolumn{2}{c}{**CSE-CIC-IDS2018**} & \multicolumn{2}{c}{**BoT-IoT**} & \multicolumn{2}{c}{**ToN-IoT**} \\ \cline{2-9}  & **Acc (\%)** & **F1 (\%)** & **Acc (\%)** & **F1 (\%)** & **Acc (\%)** & **F1 (\%)** & **Acc (\%)** & **F1 (\%)** \\ \hline E-GraphSAGE [13] & 98.73 & 98.81 & 98.70 & 98.72 & 93.76 & 95.45 & 81.47 & 82.99 \\ E-GCN & 98.73 & 98.81 & 79.01 & 82.46 & 94.35 & 95.73 & 79.79 & 80.20 \\ Anomal-E [14] & 97.77 & 97.74 & 97.89 & 97.81 & 2.31 & 2.26 & 19.57 & 16.37 \\ AH-EAT & 96.02 & 94.08 & 88.03 & 82.43 & 98.44 & 98.05 & 80.42 & 71.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the coarse-grained performance of the model on different datasets. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**UNSW-NB15**} & \multicolumn{2}{c}{**CSE-CIC-IDS2018**} & \multicolumn{2}{c}{**BoT-IoT**} & \multicolumn{2}{c}{**ToN-IoT**} \\ \cline{2-9}  & **Acc (\%)** & **F1 (\%)** & **Acc (\%)** & **F1 (\%)** & **Acc (\%)** & **F1 (\%)** & **Acc (\%)** & **F1 (\%)** \\ \hline E-GraphSAGE [13] & 95.44 & 95.92 & 9.41 & 11.35 & 76.28 & 77.02 & 49.36 & 51.13 \\ E-GCN & 94.99 & 95.40 & 1.41 & 1.29 & 78.99 & 78.51 & 11.49 & 2.52 \\ AH-EAT & 95.57 & 95.52 & 22.77 & 34.34 & 81.46 & 83.84 & 42.25 & 25.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the fine-grained performance of the model on different datasets. To further illustrate AH-EAT's effectiveness across datasets, we conducted a false positive/negative rate analysis. As shown in Figure 2c, traditional network datasets CIC-IDS2018 and UNSW-NB15 exhibit superior performance, attributed to their diverse protocols (e.g., HTTP, DNS) and complex attack patterns. These enable AH-EAT's edge attention mechanism to capture topological differences and avoid attack-type confusion. Specifically, CIC-IDS2018 achieves a perfect True Positive Rate (TPR = 1.0) with a near-zero False Positive Rate (FPR) and False Negative Rate (FNR), while UNSW-NB15 shows a high TPR of 0.9477 with low FPR (0.0046) and FNR (0.0523). Conversely, AH-EAT's poor performance on ToN-IoT is reaffirmed by its relatively high FNR, indicating limited feature diversity in this IoT dataset. BoT-IoT, another IoT dataset, outperforms ToN-IoT but still lags behind traditional datasets, highlighting that the constrained feature space of IoT data--dominated by basic UDP/TCP protocols--hinders discriminability compared to rich traditional network flows. To gain insights into the underlying mechanisms of AH-EAT's performance, we conducted an analysis of the model's learned features. The edge attention mechanism in AH-EAT assigns varying weights to different edges in the network flow graph, with higher weights typically given to edges that exhibit abnormal flow statistics, such as unusually high packet counts or rare port pairs. This indicates that the model has learned to prioritize attack propagation paths and critical topological interactions, which are essential for distinguishing malicious flows from benign ones.