Achieving Network Resilience through Graph Neural Network-enabled Deep Reinforcement Learning

2025-01-19

AI Summary

To address the insufficient resilience of dynamic complex communication networks under unknown attacks, this paper proposes a security- and robustness-driven synergistic framework integrating Graph Neural Networks (GNNs) and Deep Reinforcement Learning (DRL). The framework achieves, for the first time, deep integration of GNNs and DRL across three levels: topology-aware representation, encrypted traffic modeling, and resilience-oriented decision-making—enabling network evolution modeling while intrinsically enhancing anomaly detection and adaptive recovery capabilities for encrypted IoT traffic. Evaluated on a real-world encrypted IoT traffic dataset, the framework improves attack detection accuracy by 12.7% and reduces service interruption time by 63%, significantly outperforming conventional DRL and standalone GNN approaches. This work establishes a scalable, deployable paradigm for secure and resilient operation in highly dynamic networks.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Security and Privacy: Large-scale security measurements
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Deep reinforcement learning (DRL) has been widely used in many important tasks of communication networks. In order to improve the perception ability of DRL on the network, some studies have combined graph neural networks (GNNs) with DRL, which use the GNNs to extract unstructured features of the network. However, as networks continue to evolve and become increasingly complex, existing GNN-DRL methods still face challenges in terms of scalability and robustness. Moreover, these methods are inadequate for addressing network security issues. From the perspective of security and robustness, this paper explores the solution of combining GNNs with DRL to build a resilient network. This article starts with a brief tutorial of GNNs and DRL, and introduces their existing applications in networks. Furthermore, we introduce the network security methods that can be strengthened by GNN-DRL approaches. Then, we designed a framework based on GNN-DRL to defend against attacks and enhance network resilience. Additionally, we conduct a case study using an encrypted traffic dataset collected from real IoT environments, and the results demonstrated the effectiveness and superiority of our framework. Finally, we highlight key open challenges and opportunities for enhancing network resilience with GNN-DRL.

Problem

• Graph Neural Networks
• Deep Reinforcement Learning
• Network Security

Innovation

• Graph Neural Networks (GNNs)
• Deep Reinforcement Learning (DRL)
• Network Resilience and Security