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

TL;DR

This paper proposes a GNN-DRL framework to improve network resilience and security, demonstrating its effectiveness through a real IoT dataset and discussing future challenges and opportunities.

Contribution

It introduces a novel GNN-DRL framework specifically designed for enhancing network robustness and security against attacks.

Findings

Framework effectively defends against network attacks

Demonstrates superior performance on real IoT traffic data

Highlights open challenges in scalable GNN-DRL applications

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.