BPF-GNN: A Multi-Granularity Feature Extraction Model Using Graph Neural Networks for Encrypted Traffic Classification

Encrypted traffic classification is crucial for critical network management tasks such as traffic type identification, resource allocation, and risk mitigation, especially given that encrypted traffic has become the dominant form of modern network communication. However, existing classification methods are typically confined to single-level feature extraction, failing to capture the multi-granularity information inherent in traffic and thus limiting their ability to characterize complex encrypted traffic patterns. To address this issue, this paper proposes BPF-GNN, a hierarchical graph feature extraction model for encrypted traffic classification. The model enables multi-granularity feature learning by constructing a three-tier graph structure (Byte-, Packet-, and Flow-level). It sequentially extracts discriminative information inherent in each granularity level and accumulates multi-dimensional traffic characteristics, significantly improving the classification accuracy of encrypted traffic. Experiments on the ISCX-VPN2016, ISCX-Tor2016, USTC-TFC2016, and MIRAGE-2024 datasets demonstrate that BPF-GNN outperforms existing methods, validating the effectiveness and superiority of the proposed hierarchical multi-granularity feature extraction approach.