Video violence detection plays a crucial role in intelligent surveillance and public safety, yet existing methods still face challenges in modeling complex multi-person interactions. To address this, we propose IDG-ViolenceNet, a dual-stream video violence detection model that integrates identity-aware spatiotemporal graphs with three-dimensional convolutional neural networks (3D-CNN). Specifically, the model utilizes YOLOv11 for high-precision person detection and cross-frame identity tracking, constructing a dynamic spatiotemporal graph that encodes spatial proximity, temporal continuity, and individual identity information. On this basis, a GINEConv branch extracts structured interaction features, while an R3D-18 branch models local spatiotemporal patterns. The two representations are fused in a dedicated module for cross-modal feature integration. Experimental results show that IDG-ViolenceNet achieves accuracies of 97.5%, 99.5%, and 89.4% on the Hockey Fight, Movies Fight, and RWF-2000 datasets, respectively, significantly outperforming state-of-the-art methods. Additionally, ablation studies validate the contributions of key components in improving detection accuracy and robustness.

With the widespread adoption of encryption technologies, encrypted traffic classification has become essential for maintaining network security awareness and optimizing service quality. However, existing deep learning-based methods often rely on fixed-length truncation during preprocessing, which can lead to the loss of critical information and degraded classification performance. To address this issue, we propose a Multi-Feature Fusion (MFF) model that learns robust representations of encrypted traffic through a dual-path feature extraction architecture. The temporal modeling branch incorporates a Squeeze-and-Excitation (SE) attention mechanism into ResNet18 to dynamically emphasize salient temporal patterns. Meanwhile, the global statistical feature branch uses an autoencoder for the nonlinear dimensionality reduction and semantic reconstruction of 52-dimensional statistical features, effectively preserving high-level semantic information of traffic interactions. MFF integrates both feature types to achieve feature enhancement and construct a more robust representation, thereby improving classification accuracy and generalization. In addition, SHAP-based interpretability analysis further validates the model’s decision-making process and reliability. Experimental results show that MFF achieves classification accuracies of 99.61% and 99.99% on the ISCX VPN-nonVPN and USTC-TFC datasets, respectively, outperforming mainstream baselines.