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

TL;DR

This paper introduces GSID, a graph-based anomaly detection model that improves network resilience by identifying protocol configuration anomalies through structural inconsistency detection in bipartite graphs.

Contribution

The paper proposes the GSID model with adaptive encoding and dynamic attention mechanisms, achieving significant improvements over existing anomaly detection methods.

Findings

GSID outperforms baselines by threefold in F1 score.

GSID improves accuracy by 23.2%.

Ablation studies confirm the effectiveness of ACE and IDA modules.

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.