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

• URL: http://arxiv.org/abs/2501.11074v1
• Date: Sun, 19 Jan 2025 15:22:17 GMT
• Title: Achieving Network Resilience through Graph Neural Network-enabled Deep Reinforcement Learning
• Authors: Xuzeng Li, Tao Zhang, Jian Wang, Zhen Han, Jiqiang Liu, Jiawen Kang, Dusit Niyato, Abbas Jamalipour,
• Abstract summary: Deep reinforcement learning (DRL) has been widely used in many important tasks of communication networks. Some studies have combined graph neural networks (GNNs) with DRL, which use the GNNs to extract unstructured features of the network. This paper explores the solution of combining GNNs with DRL to build a resilient network.
• Score: 64.20847540439318
• License:
• 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.

Related papers

• Investigating Application of Deep Neural Networks in Intrusion Detection System Design [0.0]
  Research aims to learn how effective applications of Deep Neural Networks (DNN) can accurately detect and identify malicious network intrusion. Test results demonstrate no support for the model to accurately and correctly distinguish the classification of network intrusion.
  arXiv  Detail & Related papers  (2025-01-27T04:06:30Z)
• A Survey for Deep Reinforcement Learning Based Network Intrusion Detection [3.493620624883548]
  This paper explores the potential and challenges of using deep reinforcement learning (DRL) in network intrusion detection. The performance of DRL models is analyzed, showing that while DRL holds promise, many recent technologies remain underexplored. The paper concludes with recommendations for enhancing DRL deployment and testing in real-world network scenarios.
  arXiv  Detail & Related papers  (2024-09-25T13:39:30Z)
• Survey of Graph Neural Network for Internet of Things and NextG Networks [3.591122855617648]
  Graph Neural Networks (GNNs) have emerged as a promising paradigm for effectively modeling and extracting insights. This survey provides a detailed description of GNN's terminologies, architecture, and the different types of GNNs. Next, we provide a detailed account of how GNN has been leveraged for networking and tactical systems.
  arXiv  Detail & Related papers  (2024-05-27T16:10:49Z)
• Model-Agnostic Reachability Analysis on Deep Neural Networks [25.54542656637704]
  We develop a model-agnostic verification framework, called DeepAgn. It can be applied to FNNs, Recurrent Neural Networks (RNNs), or a mixture of both. It does not require access to the network's internal structures, such as layers and parameters.
  arXiv  Detail & Related papers  (2023-04-03T09:01:59Z)
• Deep Binary Reinforcement Learning for Scalable Verification [44.44006029119672]
  We present an RL algorithm tailored specifically for binarized neural networks (BNNs) After training BNNs for the Atari environments, we verify robustness properties.
  arXiv  Detail & Related papers  (2022-03-11T01:20:23Z)
• Deep Reinforcement Learning with Spiking Q-learning [51.386945803485084]
  spiking neural networks (SNNs) are expected to realize artificial intelligence (AI) with less energy consumption. It provides a promising energy-efficient way for realistic control tasks by combining SNNs with deep reinforcement learning (RL)
  arXiv  Detail & Related papers  (2022-01-21T16:42:11Z)
• Edge Rewiring Goes Neural: Boosting Network Resilience via Policy Gradient [62.660451283548724]
  ResiNet is a reinforcement learning framework to discover resilient network topologies against various disasters and attacks. We show that ResiNet achieves a near-optimal resilience gain on multiple graphs while balancing the utility, with a large margin compared to existing approaches.
  arXiv  Detail & Related papers  (2021-10-18T06:14:28Z)
• Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks [98.21130211336964]
  Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks. In this paper, we investigate the impact of network width and depth on the robustness of adversarially trained DNNs.
  arXiv  Detail & Related papers  (2021-10-07T23:13:33Z)
• Learning Autonomy in Management of Wireless Random Networks [102.02142856863563]
  This paper presents a machine learning strategy that tackles a distributed optimization task in a wireless network with an arbitrary number of randomly interconnected nodes. We develop a flexible deep neural network formalism termed distributed message-passing neural network (DMPNN) with forward and backward computations independent of the network topology.
  arXiv  Detail & Related papers  (2021-06-15T09:03:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.