X-GANet: An Explainable Graph-Based Framework for Robust Network Intrusion Detection
Modern networks are increasingly targeted by sophisticated cyber threats, making effective intrusion detection a critical challenge. Traditional intrusion detection systems (IDSs) often struggle with capturing the complex relationships within network traffic and suffer from a lack of explainability...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/9/5002 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Modern networks are increasingly targeted by sophisticated cyber threats, making effective intrusion detection a critical challenge. Traditional intrusion detection systems (IDSs) often struggle with capturing the complex relationships within network traffic and suffer from a lack of explainability in their decision-making processes. To address these limitations, we propose X-GANet, Explainable Graph Anomaly Network, a novel graph-based intrusion detection framework that models network traffic as a graph and leverages deep representation learning for precise threat identification. The model extracts both flow-based and structural features, aligns multi-view representations through contrastive learning, and employs a transformer-based embedding module for enhanced feature extraction. An adaptive graph detection fusion mechanism ensures effective graph-level representation and detection techniques for robust attack classification. Experimental results obtained from benchmark intrusion detection datasets demonstrate that X-GANet significantly improves detection performance while maintaining interpretability, making it a promising solution for real-world cybersecurity applications. |
|---|---|
| ISSN: | 2076-3417 |