# Adversarial Hierarchical-Aware Edge Attention Learning Method for Network Intrusion Detection
## 5 Experiments and Results
### Ablation Study
It is proved that the hierarchical framework effectively protects fine-grained modules \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**Performance (\%)**} \\ \cline{2-6}  & **Accuracy** & **F1-Score** & **Robust Acc** & **Robust F1** & **ASR** \\ \hline AH-EAT & 81.46 & 83.84 & 81.15 & 83.52 & 18.85 \\ AH-EAT-NA & 82.26 & 82.68 & 82.03 & 82.54 & 17.97 \\ AH-EAT-NH & 78.54 & 69.09 & 78.18 & 68.92 & 21.82 \\ AH-EAT-NADV & 78.90 & 77.10 & 72.81 & 71.01 & 27.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablation study of model components on BoT-IoT (fine-grained). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**Performance (\%)**} \\ \cline{2-5}  & **Accuracy** & **F1-Score** & **Robust Acc** & **Robust F1** & **ASR** \\ \hline AH-EAT & 97.45 & 97.23 & 97.36 & 97.10 & 2.64 \\ AH-EAT-NA & 97.09 & 96.67 & 97.12 & 96.73 & 2.88 \\ AH-EAT-NH & 96.02 & 94.08 & 96.02 & 94.08 & 3.98 \\ AH-EAT-NADV & 11.97 & 2.56 & 11.97 & 2.56 & 88.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Ablation study of model components on UNSW-NB15 (coarse-grained). from noise interference through coarse-grained screening (filtering 85% of benign flow) and improves the identification accuracy of key attack paths. Adversarial training (AH-EAT-NADV): after closing, robust F1 drops by 12.51% (83.52%\(\rightarrow\)71.01%), and ASR surges by 8.34% (18.85%\(\rightarrow\)27.19%). It shows that adversarial training improves the model's defense capability against edge feature perturbations by more than 3 times, highlighting the necessity of adversarial training. Table 7 shows the effects on a coarse-grained dataset. Attention mechanism (AH-EAT-NA): the F1-score drops by 0.56% (97.23%\(\rightarrow\)96.67%) and ASR increases slightly by 0.24% (2.64%\(\rightarrow\)2.88%). It shows that in coarse-grained tasks, global topological information is more important than local attention. However, the attention mechanism still contributes to the recognition ability of key information. Hierarchical detection framework (AH-EAT-NH): direct multi-classification leads to a 3.15% drop in F1-score (97.23%\(\rightarrow\)94.08%), and a 1.34% increase in ASR (2.64%\(\rightarrow\)3.98%). Adversarial training (AH-EAT-NADV): after turning it off, the model completely collapses (robust F1 = 2.56% vs. 97.10%), and ASR soars to 88.03%. It proves that adversarial training is the core guarantee of robustness. It enables the model to learn the protocol semantic invariance (such as HTTP method legality constraints) through edge feature perturbation generation. In summary, the attention mechanism provides high-discrimination features for hierarchical detection, and the hierarchical framework reduces the interference of invalid samples in adversarial training (such as fine-grained adversarial optimization only for samples that are judged as attacks at a coarse-grained level). Adversarial training feeds back feature learning, forming a positive cycle of \"feature screening \(\rightarrow\) hierarchical decision \(\rightarrow\) robust enhancement\".