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

TL;DR

This paper addresses the challenge of building resilient networks in the face of growing scale and security threats by integrating Graph Neural Networks (GNNs) with Deep Reinforcement Learning (DRL). It surveys fundamental GNN and DRL methods, and discusses how their combination (GNN-DRL) enhances network representation, routing, and virtual network management, with a focus on security applications such as attack detection, security assessment, and defense. A dynamic GNN-DRL framework is proposed, comprising modeling, decision-making, and execution phases, and a case study using encrypted IoT traffic demonstrates improved resilience against floods and brute-force attacks, outperforming baselines and showing feasible runtime efficiency. The paper also outlines key challenges—scalability, real-time constraints, multi-objective trade-offs, and robustness—and presents future directions for large-scale, distributed, resource-constrained, and highly dynamic networks, highlighting practical implications for secure network operation and management.

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.

Figures (6)

• Figure 1: Comparison with existing researches on GNN and DRL in communication networks. We investigate the related works and select articles from the past two years that are closely related to GNN-DRL and communication networks for comparison. The index in the first row of the table indicate whether the research analyzed GNN, DRL, and network security. Most current studies focus on combining GNNs and DRL to improve the QoS, while research on using GNN-DRL to address network security and improve network resilience remains limited.
• Figure 2: Analysis of different combinations of GNNs and DRL in communication networks. We selected representative models from GNNs (GCN, GraphSAGE and GAT), and combined them with different categories of DRL methods (DQN, PPO, DDPG and SAC) for analysis. Our findings suggest that GNNs can enhance the modeling and the feature extraction capability of DRL. Besides, GNN can improve the applicability of DRL across various network tasks.
• Figure 3: The schematic of building a resilient network based on GNN-DRL. Existing GNN-DRL methods for networks are vulnerable to diverse attacks. Moreover, designing GNN-DRL models for complex networks presents challenges such as model scalability, computational complexity, multi-objective optimization, and robustness. To address these issues, we explore GNN-DRL-based approaches aimed at enhancing network resilience by improving attack detection, risk assessment, and defense mechanisms.
• Figure 4: The framework of GNN-DRL for building secure and resilient networks. The framework comprises three phases: modeling, decision-making, and execution. During the modeling and decision-making phases, data processing is conducted, where network relationships are represented using graphs, and GNN-DRL methods are employed to generate optimized network configuration policies. In the decision-making phase, the network configuration is continuously refined based on the current network environment. This process involves generating configuration policies at the network management layer, which are subsequently applied to the network. The network then adjusts according to the optimized policies and provides feedback regarding the environment state.
• Figure 5: GNN-DRL for building a resilient and secure network. We utilize network topology and traffic as inputs, modeling encrypted traffic within the network using graphs. By applying GCN, we can effectively identify different types of malicious encrypted traffic. Additionally, we employ Bayesian methods to achieve a more accurate representation of the environmental status. Ultimately, we use DQN to generate routing policies that help avoid attacks.