# Adversarial Hierarchical-Aware Edge Attention Learning Method for Network Intrusion Detection
## 5 Experiments and Results
### Comparison with SOTA Methods  
The coarse-grained and fine-grained classification capabilities of GNNs are crucial in the field of network security. Tables 2 and 3 show the performance comparison of different models on coarse-grained (binary classification: normal/attack) and fine-grained (multi-classification: specific attack type) tasks, respectively. In coarse-grained detection, AH-EAT shows significant advantages on the IoT datasets BoT-IoT and ToN-IoT. For BoT-IoT, AH-EAT's accuracy (98.44%) and F1-score (98.05%) are both higher than E-GraphSAGE (93.76%, 95.45%) and E-GCN (94.35%, 95.73%), indicating that it has a stronger ability to identify complex attacks in IoT environments. On the ToN-IoT dataset, although E-GraphSAGE has a slightly higher accuracy (81.47% vs. 80.42%), AH-EAT's F1-score (71.68%) far exceeds Anomal-E (16.37%), showing better classification balance. Anomal-E completely fails on BoT-IoT and ToN-IoT (F1 \(<\) 20%) due to its unsupervised design. Since these two datasets were not experimented on in the original Anomal-E paper, the unsupervised method Anomal-E is not adaptable enough to IoT data and the limitations of the algorithm. Fine-grained classification can better reflect the model's ability to identify attack subclasses. Since Anomal-E cannot support fine-grained tasks, Table 3 focuses on the comparison of E-GraphSAGE, E-GCN, and AH-EAT. AH-EAT's fine-grained accuracy \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Characteristics** & **E-GCN** & **E-GraphSAGE**[13] & **Anomal-E**[14] & **AH-EAT** \\ \hline Model architecture & GCN & GraphSAGE & GraphSAGE + DGI & EAT + PGD \\ \hline Edge feature utilization & ✓ & ✓ & ✓ & ✓ \\ \hline Topology utilization & ✓ & ✓ & ✓ & ✓ \\ \hline Classification granularity & Anomaly detection & Anomaly detection & Anomaly detection & Hierarchical Multi-grained \\ \hline Learning paradigm & Supervised & Supervised & Self-supervised & Supervised \\ \hline Adversarial robustness & \(\times\) & \(\times\) & \(\times\) & ✓ \\ \hline \hline \end{tabular} Note. ✓ indicates full support, \(\times\) indicates no support.  
\end{table} 
Table 1: Comparative analysis of NIDSs methods. (22.77%) and F1-score (34.34%) on the CSE-CIC-IDS2018 dataset are significantly higher than E-GraphSAGE (9.41%, 11.35%) and E-GCN (1.41%, 1.29%), indicating that it has stronger feature extraction capabilities in complex attack scenarios. In BoT-IoT and ToN-IoT, AH-EAT's fine-grained F1-scores reach 83.84% and 25.38%, respectively, far exceeding similar models. Overall, AH-EAT effectively balances the generalization of coarse-grained detection and the specificity of fine-grained classification through hierarchical detection mechanisms, so that it can perform very well in most cases for both coarse and fine granularity scenarios. Compared with the self-supervised method Anomal-E, it shows better adaptability; compared with traditional GNN models (such as E-GraphSAGE, E-GCN), its innovative edge-based graph attention network and feature fusion strategies significantly improve the ability to identify attack types, providing a more reliable solution for actual network security deployment. Since AH-EAT does not perform well on the ToN-IoT dataset in fine-grained detection, we focus on analyzing Figure 2a,b (t-SNE visualization). T-SNE visualization reveals key insights into AH-EAT's performance limitations on ToN-IoT. With more than 80% attack samples across nine fine-grained types (e.g., +0.1% Ransomware), ToN-IoT imposes extreme classification imbalance. While t-SNE shows clear separation between benign and attack clusters at the coarse-grained level, attack clusters overlap significantly due to semantically similar flow patterns (e.g., DoS and DDoS). Compared to traditional network flow datasets, the Internet of Things dataset exhibits fewer features. Most of them are dominated by basic UDP/TCP protocols and repetitive traffic timings. This forces AH-EAT to struggle with intra-attack-type discrimination.