Diffusion-enhanced Intrusion Detection Method for Imbalanced Network Traffic

Network intrusion detection systems face two critical challenges that significantly limit their effectiveness. Severe class imbalance leads to poor recognition of minority class attacks, while existing methods fail to adequately model dynamic spatiotemporal dependencies in network traffic. We propose DGT-IDS, a framework with three key innovations. First, we develop a diffusion-guided conditional GAN with multi-conditional control that leverages class labels, sample density, and diffusion timesteps as generation guidance for targeted minority class augmentation. Second, we design a spatiotemporal graph Transformer with semantic-based dynamic graph construction that computes adjacency matrices from feature semantics rather than fixed topologies. Third, we construct a meta-learning adaptive loss that eliminates static hyperparameters through bilevel optimization. Experiments on NSL-KDD and CIC-IDS2017 datasets demonstrate superior performance. On NSL-KDD, we achieve 88.27% F1-score and 90.79% recall. On CIC-IDS2017, we achieve 92.84% F1-score and 93.51% recall. Our method outperforms representative baselines while improving minority class detection.