Abstract: Traditional anomalous traffic detection methods, based on single-view analysis, have obvious limitations in dealing with complex attacks and encrypted communications. To address this, the paper proposes a network anomaly traffic detection method via multi-view feature fusion (MuFF). 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.