NetFlowGen: Leveraging Generative Pre-training for Network Traffic Dynamics

• URL: http://arxiv.org/abs/2412.20635v1
• Date: Mon, 30 Dec 2024 00:47:49 GMT
• Title: NetFlowGen: Leveraging Generative Pre-training for Network Traffic Dynamics
• Authors: Jiawei Zhou, Woojeong Kim, Zhiying Xu, Alexander M. Rush, Minlan Yu,
• Abstract summary: We propose to pre-train a general-purpose machine learning model to capture traffic dynamics with only traffic data from NetFlow records. We address challenges such as unifying network feature representations, learning from large unlabeled traffic data volume, and testing on real downstream tasks in DDoS attack detection.
• Score: 72.95483148058378
• License:
• Abstract: Understanding the traffic dynamics in networks is a core capability for automated systems to monitor and analyze networking behaviors, reducing expensive human efforts and economic risks through tasks such as traffic classification, congestion prediction, and attack detection. However, it is still challenging to accurately model network traffic with machine learning approaches in an efficient and broadly applicable manner. Task-specific models trained from scratch are used for different networking applications, which limits the efficiency of model development and generalization of model deployment. Furthermore, while networking data is abundant, high-quality task-specific labels are often insufficient for training individual models. Large-scale self-supervised learning on unlabeled data provides a natural pathway for tackling these challenges. We propose to pre-train a general-purpose machine learning model to capture traffic dynamics with only traffic data from NetFlow records, with the goal of fine-tuning for different downstream tasks with small amount of labels. Our presented NetFlowGen framework goes beyond a proof-of-concept for network traffic pre-training and addresses specific challenges such as unifying network feature representations, learning from large unlabeled traffic data volume, and testing on real downstream tasks in DDoS attack detection. Experiments demonstrate promising results of our pre-training framework on capturing traffic dynamics and adapting to different networking tasks.

Related papers

• Towards a graph-based foundation model for network traffic analysis [3.0558245652654907]
  Foundation models can grasp the complexities of network traffic dynamics and adapt to any specific task or environment with minimal fine-tuning. Previous approaches have used tokenized hex-level packet data. We propose a new, efficient graph-based alternative at the flow-level.
  arXiv  Detail & Related papers  (2024-09-12T15:04:34Z)
• Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch [72.26822499434446]
  Auto-Train-Once (ATO) is an innovative network pruning algorithm designed to automatically reduce the computational and storage costs of DNNs. We provide a comprehensive convergence analysis as well as extensive experiments, and the results show that our approach achieves state-of-the-art performance across various model architectures.
  arXiv  Detail & Related papers  (2024-03-21T02:33:37Z)
• Lens: A Foundation Model for Network Traffic [19.3652490585798]
  Lens is a foundation model for network traffic that leverages the T5 architecture to learn the pre-trained representations from large-scale unlabeled data. We design a novel loss that combines three distinct tasks: Masked Span Prediction (MSP), Packet Order Prediction (POP), and Homologous Traffic Prediction (HTP)
  arXiv  Detail & Related papers  (2024-02-06T02:45:13Z)
• NetGPT: Generative Pretrained Transformer for Network Traffic [4.205009931131087]
  Pretrained models for network traffic can utilize large-scale raw data to learn the essential characteristics of network traffic. In this paper, we make the first attempt to provide a generative pretrained model NetGPT for both traffic understanding and generation tasks.
  arXiv  Detail & Related papers  (2023-04-19T09:04:30Z)
• Interference Cancellation GAN Framework for Dynamic Channels [74.22393885274728]
  We introduce an online training framework that can adapt to any changes in the channel. Our framework significantly outperforms recent neural network models on highly dynamic channels.
  arXiv  Detail & Related papers  (2022-08-17T02:01:18Z)
• Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition [70.306089187104]
  We introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well.
  arXiv  Detail & Related papers  (2022-05-28T03:11:48Z)
• Data-Driven Traffic Assignment: A Novel Approach for Learning Traffic Flow Patterns Using a Graph Convolutional Neural Network [1.3706331473063877]
  We present a novel data-driven approach of learning traffic flow patterns of a transportation network. We develop a neural network-based framework known as Graph Convolutional Neural Network (GCNN) to solve it. When the training of the model is complete, it can instantly determine the traffic flows of a large-scale network.
  arXiv  Detail & Related papers  (2022-02-21T19:45:15Z)
• Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks [79.28094304325116]
  Graph Convolutional Networks are among the most promising approaches for capturing relationships among structured data points. We propose three novel self-supervised auxiliary tasks to train graph-based neural network models in a multi-task fashion.
  arXiv  Detail & Related papers  (2020-11-14T11:09:51Z)
• Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks [1.0934800950965335]
  We develop a nonautore graph-based neural network for multistep network traffic forecasting. We evaluate the efficacy of our approach on real traffic from ESnet, the U.S. Department of Energy's dedicated science network.
  arXiv  Detail & Related papers  (2020-08-28T17:47:11Z)
• Multi-Agent Routing Value Iteration Network [88.38796921838203]
  We propose a graph neural network based model that is able to perform multi-agent routing based on learned value in a sparsely connected graph. We show that our model trained with only two agents on graphs with a maximum of 25 nodes can easily generalize to situations with more agents and/or nodes.
  arXiv  Detail & Related papers  (2020-07-09T22:16:45Z)

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.