Beyond 5G Network Failure Classification for Network Digital Twin Using Graph Neural Network

• URL: http://arxiv.org/abs/2406.06595v1
• Date: Thu, 6 Jun 2024 07:36:25 GMT
• Title: Beyond 5G Network Failure Classification for Network Digital Twin Using Graph Neural Network
• Authors: Abubakar Isah, Ibrahim Aliyu, Jaechan Shim, Hoyong Ryu, Jinsul Kim,
• Abstract summary: Fifth-generation (5G) core networks in network digital twins (NDTs) are complex systems with numerous components, generating considerable data. We propose a novel method of integrating a graph Fourier transform (GFT) into a message-passing neural network (MPNN) designed for NDTs. This approach identifies failure types in real and simulated NDT environments, demonstrating its potential for accurate failure classification in 5G and beyond networks.
• Score: 0.4660328753262075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fifth-generation (5G) core networks in network digital twins (NDTs) are complex systems with numerous components, generating considerable data. Analyzing these data can be challenging due to rare failure types, leading to imbalanced classes in multiclass classification. To address this problem, we propose a novel method of integrating a graph Fourier transform (GFT) into a message-passing neural network (MPNN) designed for NDTs. This approach transforms the data into a graph using the GFT to address class imbalance, whereas the MPNN extracts features and models dependencies between network components. This combined approach identifies failure types in real and simulated NDT environments, demonstrating its potential for accurate failure classification in 5G and beyond (B5G) networks. Moreover, the MPNN is adept at learning complex local structures among neighbors in an end-to-end setting. Extensive experiments have demonstrated that the proposed approach can identify failure types in three multiclass domain datasets at multiple failure points in real networks and NDT environments. The results demonstrate that the proposed GFT-MPNN can accurately classify network failures in B5G networks, especially when employed within NDTs to detect failure types.

Related papers

• A GNN-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twin [1.3744158081557412]
  Digital twins of 5G networks increasingly employ graph classification as the main method for identifying failure types. The skewed distribution of failure occurrences is a major class imbalance issue that prevents effective graph data mining. We propose Class-Fourier Graph Neural Network (CF-GNN) introduces a class-oriented spectral filtering mechanism that ensures precise classification.
  arXiv  Detail & Related papers  (2025-02-17T07:12:39Z)
• Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond [5.452584641316627]
  6G/NextG networks may become vulnerable to new security threats. Existing studies on the intrusion detection task rely on the train of shallow machine learning classifiers. We present the first study integrating Mixture of Experts (MoE) for identifying malicious traffic.
  arXiv  Detail & Related papers  (2024-12-04T17:20:01Z)
• DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment [57.62885438406724]
  Graph neural networks are recognized for their strong performance across various applications. BP has limitations that challenge its biological plausibility and affect the efficiency, scalability and parallelism of training neural networks for graph-based tasks. We propose DFA-GNN, a novel forward learning framework tailored for GNNs with a case study of semi-supervised learning.
  arXiv  Detail & Related papers  (2024-06-04T07:24:51Z)
• CNN2GNN: How to Bridge CNN with GNN [59.42117676779735]
  We propose a novel CNN2GNN framework to unify CNN and GNN together via distillation. The performance of distilled boosted'' two-layer GNN on Mini-ImageNet is much higher than CNN containing dozens of layers such as ResNet152.
  arXiv  Detail & Related papers  (2024-04-23T08:19:08Z)
• BLIS-Net: Classifying and Analyzing Signals on Graphs [20.345611294709244]
  Graph neural networks (GNNs) have emerged as a powerful tool for tasks such as node classification and graph classification. We introduce the BLIS-Net (Bi-Lipschitz Scattering Net), a novel GNN that builds on the previously introduced geometric scattering transform. We show that BLIS-Net achieves superior performance on both synthetic and real-world data sets based on traffic flow and fMRI data.
  arXiv  Detail & Related papers  (2023-10-26T17:03:14Z)
• Graph Neural Networks for Power Allocation in Wireless Networks with Full Duplex Nodes [10.150768420975155]
  Due to mutual interference between users, power allocation problems in wireless networks are often non-trivial. Graph Graph neural networks (GNNs) have recently emerged as a promising approach tackling these problems and an approach exploits underlying topology of wireless networks.
  arXiv  Detail & Related papers  (2023-03-27T10:59:09Z)
• Scaling Graph-based Deep Learning models to larger networks [2.946140899052065]
  Graph Neural Networks (GNN) have shown a strong potential to be integrated into commercial products for network control and management. This paper presents a GNN-based solution that can effectively scale to larger networks including higher link capacities and aggregated traffic on links.
  arXiv  Detail & Related papers  (2021-10-04T09:04:19Z)
• An Introduction to Robust Graph Convolutional Networks [71.68610791161355]
  We propose a novel Robust Graph Convolutional Neural Networks for possible erroneous single-view or multi-view data. By incorporating an extra layers via Autoencoders into traditional graph convolutional networks, we characterize and handle typical error models explicitly.
  arXiv  Detail & Related papers  (2021-03-27T04:47:59Z)
• A Biased Graph Neural Network Sampler with Near-Optimal Regret [57.70126763759996]
  Graph neural networks (GNN) have emerged as a vehicle for applying deep network architectures to graph and relational data. In this paper, we build upon existing work and treat GNN neighbor sampling as a multi-armed bandit problem. We introduce a newly-designed reward function that introduces some degree of bias designed to reduce variance and avoid unstable, possibly-unbounded payouts.
  arXiv  Detail & Related papers  (2021-03-01T15:55:58Z)
• Stochastic Graph Neural Networks [123.39024384275054]
  Graph neural networks (GNNs) model nonlinear representations in graph data with applications in distributed agent coordination, control, and planning. Current GNN architectures assume ideal scenarios and ignore link fluctuations that occur due to environment, human factors, or external attacks. In these situations, the GNN fails to address its distributed task if the topological randomness is not considered accordingly.
  arXiv  Detail & Related papers  (2020-06-04T08:00:00Z)
• Binarized Graph Neural Network [65.20589262811677]
  We develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters. Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches. Experiments indicate that the proposed binarized graph neural network, namely BGN, is orders of magnitude more efficient in terms of both time and space.
  arXiv  Detail & Related papers  (2020-04-19T09:43:14Z)

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.