PPT-GNN: A Practical Pre-Trained Spatio-Temporal Graph Neural Network for Network Security
This work addresses practical deployment issues in network security for real-world applications, though it appears incremental as it builds on existing GNN methods with enhancements for speed and generalization.
The paper tackles the problem of slow and non-generalizable network intrusion detection by introducing PPTGNN, a spatio-temporal graph neural network that enables near real-time predictions and achieves an average accuracy improvement of 10.38% over state-of-the-art models on three public datasets.
Recent works have demonstrated the potential of Graph Neural Networks (GNN) for network intrusion detection. Despite their advantages, a significant gap persists between real-world scenarios, where detection speed is critical, and existing proposals, which operate on large graphs representing several hours of traffic. This gap results in unrealistic operational conditions and impractical detection delays. Moreover, existing models do not generalize well across different networks, hampering their deployment in production environments. To address these issues, we introduce PPTGNN, a practical spatio-temporal GNN for intrusion detection. PPTGNN enables near real-time predictions, while better capturing the spatio-temporal dynamics of network attacks. PPTGNN employs self-supervised pre-training for improved performance and reduced dependency on labeled data. We evaluate PPTGNN on three public datasets and show that it significantly outperforms state-of-the-art models, such as E-ResGAT and E-GraphSAGE, with an average accuracy improvement of 10.38%. Finally, we show that a pre-trained PPTGNN can easily be fine-tuned to unseen networks with minimal labeled examples. This highlights the potential of PPTGNN as a general, large-scale pre-trained model that can effectively operate in diverse network environments.