Disentangled Dynamic Intrusion Detection

Network-based intrusion detection system (NIDS) monitors network traffic for malicious activities, formingthe frontline defense against increasing attacks over information infrastructures. Although promising, our quantitative analysis shows that existing methods perform inconsistently in attacks (e.g., 18% F1 for the MITM and 93% F1 for DDoS by a GCN-based state-of-the-art method), and perform poorly in few-shot intrusion detections (e.g., dramatically drops from 91% to 36% in 3D-IDS, and drops from 89% to 20% in E-GraphSAGE). We reveal that the underlying cause is entangled distributions of flow features. This motivates us to propose DIDS-MFL, a disentangled intrusion detection approach for various scenarios. DIDS-MFL involves two key components: a double Disentanglement-based Intrusion Detection System (DIDS) and a plug-and-play Multi-scale Few-shot Learning-based (MFL) intrusion detection module. Specifically, the proposed DIDS first disentangles traffic features by a non-parameterized optimization, automatically differentiating tens and hundreds of complex features. Such differentiated features will be further disentangled to highlight the attack-specific features. Our DIDS additionally uses a novel graph diffusion method that dynamically fuses the network topology for spatial-temporal aggregation in evolving data streams. Furthermore, the proposed MFL involves an alternating optimization framework to address the entangled representations in few-shot traffic threats with rigorous derivation. MFL first captures multi-scale information in latent space to distinguish attack-specific information and then optimizes the disentanglement term to highlight the attack-specific information. Finally, MFL fuses and alternately solves them in an end-to-end way. To the best of our knowledge, DIDS-MFL takes the first step toward disentangled dynamic intrusion detection under various attack scenarios. Equipped with DIDS-MFL, administrators can effectively identify various attacks in encrypted traffic, including known, unknown, and few-shot threats that are not easily detected. Comprehensive experiments show the superiority of our proposed DIDS-MFL. For few-shot NIDS, our DIDS-MFL achieves a 71.91% -125.19% improvement in average F1-score over 14 baselines and shows versatility in multiple baselines and multiple tasks.