Network Anomaly Traffic Detection via Multi-view Feature Fusion
This addresses limitations in detecting complex attacks and encrypted communications for network security, but appears incremental as it builds on existing multi-view approaches.
The paper tackles the problem of detecting anomalous network traffic by proposing a Multi-view Feature Fusion (MuFF) method that models temporal and interactive relationships, showing excellent performance on six real datasets.
Traditional anomalous traffic detection methods are based on single-view analysis, which has obvious limitations in dealing with complex attacks and encrypted communications. In this regard, we propose a Multi-view Feature Fusion (MuFF) method for network anomaly traffic detection. MuFF models the temporal and interactive relationships of packets in network traffic based on the temporal and interactive viewpoints respectively. It learns temporal and interactive features. These features are then fused from different perspectives for anomaly traffic detection. Extensive experiments on six real traffic datasets show that MuFF has excellent performance in network anomalous traffic detection, which makes up for the shortcomings of detection under a single perspective.