Enhancing Network Resilience via Graph-Based Anomaly Detection in Sovereign Functions

2026-05-17

AI Summary

This work addresses the critical issue of structural inconsistencies in routing protocols within sovereign networks, which often arise from misconfigurations and can lead to severe outages. To tackle this problem, the authors propose the Graph Structure Inconsistency Detector (GSID), formulating inconsistency detection as identifying structural anomalies among nodes and edges in a bipartite graph. GSID innovatively integrates an Adaptive Configuration Encoder (ACE) to handle heterogeneous configuration parameters and an Inconsistency-aware Dynamic Attention mechanism (IDA) to capture subtle topological irregularities. The approach synergistically combines graph neural networks with rule compliance checks and routing connectivity constraints. Experimental results demonstrate that GSID achieves an F1 score three times higher than the current state-of-the-art method, improves accuracy by 23.2%, and exhibits strong generalization across unseen network scales and real-world topologies.

Abstract

Sovereign network functions, e.g., routing protocols, are becoming increasingly complex and susceptible to failures arising from protocol configuration anomalies and anomalous configurations. This paper interprets the protocol configuration anomaly detection problem as detection of structural inconsistencies of connected nodes and edges in a bipartite graph that captures both physical network entities and logical protocol states. This graph structural inconsistency detector (GSID) model is proposed to solve the problem efficiently. To handle the heterogeneous nature of protocol configuration parameters, GSID employs an adaptive configuration encoder (ACE) that dynamically selects encoding strategies per parameter to preserve fine-grained numerical discrepancies. To expose the subtle inconsistencies of connected nodes and edges in the bipartite graph, GSID uses an inconsistency dynamic attention (IDA) mechanism that scores edges by drawing asymmetric attentions from both ends, rule compliance from one end and route connectivity from the other. It is demonstrated experimentally that GSID outperforms state-of-the-art baselines by threefold in F1 score and by 23.2% in accuracy. Ablation studies validate the effectiveness of both the ACE and IDA modules. Tests on unseen network scales and real-world network topologies show the superior adaptability of our GSID, compared to the baselines.

Problem

• network resilience
• protocol configuration anomalies
• anomaly detection
• sovereign network functions
• graph-based inconsistency

Innovation

• Graph-Based Anomaly Detection
• Structural Inconsistency
• Adaptive Configuration Encoder
• Dynamic Attention Mechanism
• Sovereign Network Functions