TrafficGPT: Breaking the Token Barrier for Efficient Long Traffic Analysis and Generation

• URL: http://arxiv.org/abs/2403.05822v2
• Date: Mon, 18 Mar 2024 05:11:22 GMT
• Title: TrafficGPT: Breaking the Token Barrier for Efficient Long Traffic Analysis and Generation
• Authors: Jian Qu, Xiaobo Ma, Jianfeng Li,
• Abstract summary: We introduce TrafficGPT, a deep learning model that can tackle complex challenges related to long flow classification and generation tasks. TrafficGPT demonstrates superior performance in classification tasks, reaching state-of-the-art levels. These advancements hold promise for future applications in both traffic flow classification and generation tasks.
• Score: 5.802618825302984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Over the years, network traffic analysis and generation have advanced significantly. From traditional statistical methods, the field has progressed to sophisticated deep learning techniques. This progress has improved the ability to detect complex patterns and security threats, as well as to test and optimize network performance. However, obstacles persist, such as the dependence on labeled data for analysis and the difficulty of generating traffic samples that follow realistic patterns. Pre-trained deep neural networks have emerged as powerful tools to resolve these issues, offering improved performance by learning robust data representations from large unlabeled datasets. Despite their benefits, existing pre-trained models face challenges like token length limitation, which restricts their usefulness in comprehensive traffic analysis and realistic traffic generation. To address these challenges, we introduce TrafficGPT, a deep learning model that can tackle complex challenges related to long flow classification and generation tasks. This model uses generative pre-training with the linear attention mechanism, which allows for a substantially increased capacity of up to 12,032 tokens from the previous limit of only 512 tokens. TrafficGPT demonstrates superior performance in classification tasks, reaching state-of-the-art levels. In generation tasks, it closely resembles real traffic flows, with low JS divergence and an F1 score close to 0.5 (representing a random guess) in discriminating generated data. These advancements hold promise for future applications in both traffic flow classification and generation tasks.

Related papers

• Language of Network: A Generative Pre-trained Model for Encrypted Traffic Comprehension [16.795038178588324]
  Deep learning is currently the predominant approach for encrypted traffic classification through feature analysis.<n>We present GBC, a generative model based on pre-training for encrypted traffic comprehension.<n>It achieves superior results in both traffic classification and generation tasks, resulting in a 5% improvement in F1 score compared to state-of-the-art methods for classification tasks.
  arXiv  Detail & Related papers  (2025-05-26T04:04:29Z)
• NetFlowGen: Leveraging Generative Pre-training for Network Traffic Dynamics [72.95483148058378]
  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.
  arXiv  Detail & Related papers  (2024-12-30T00:47:49Z)
• Towards Scalable and Deep Graph Neural Networks via Noise Masking [59.058558158296265]
  Graph Neural Networks (GNNs) have achieved remarkable success in many graph mining tasks. scaling them to large graphs is challenging due to the high computational and storage costs. We present random walk with noise masking (RMask), a plug-and-play module compatible with the existing model-simplification works.
  arXiv  Detail & Related papers  (2024-12-19T07:48:14Z)
• Liquid Neural Network-based Adaptive Learning vs. Incremental Learning for Link Load Prediction amid Concept Drift due to Network Failures [37.66676003679306]
  Adapting to concept drift is a challenging task in machine learning. In communication networks, such issue emerges when performing traffic forecasting following afailure event. We propose an approach that exploits adaptive learning algorithms, namely, liquid neural networks, which are capable of self-adaptation to abrupt changes in data patterns without requiring any retraining.
  arXiv  Detail & Related papers  (2024-04-08T08:47:46Z)
• Energy-Guided Data Sampling for Traffic Prediction with Mini Training Datasets [13.065729535009925]
  We propose an innovative solution that merges Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) architecture to enhance the prediction of traffic flow dynamics. A key revelation of our research is the feasibility of sampling training data for large traffic systems from simulations conducted on smaller traffic systems.
  arXiv  Detail & Related papers  (2024-03-27T15:57:42Z)
• TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models [27.306180426294784]
  We introduce TPLLM, a novel traffic prediction framework leveraging Large Language Models (LLMs) In this framework, we construct a sequence embedding layer based on Conal Neural Networks (LoCNNs) and a graph embedding layer based on Graph Contemporalal Networks (GCNs) to extract sequence features and spatial features. Experiments on two real-world datasets demonstrate commendable performance in both full-sample and few-shot prediction scenarios.
  arXiv  Detail & Related papers  (2024-03-04T17:08:57Z)
• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• 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)
• Distil the informative essence of loop detector data set: Is network-level traffic forecasting hungry for more data? [0.8002196839441036]
  We propose an uncertainty-aware traffic forecasting framework to explore how many samples of loop data are truly effective for training forecasting models. The proposed methodology proves valuable in evaluating large traffic datasets' true information content.
  arXiv  Detail & Related papers  (2023-10-31T11:23:10Z)
• LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting [65.71129509623587]
  Road traffic forecasting plays a critical role in smart city initiatives and has experienced significant advancements thanks to the power of deep learning. However, the promising results achieved on current public datasets may not be applicable to practical scenarios. We introduce the LargeST benchmark dataset, which includes a total of 8,600 sensors in California with a 5-year time coverage.
  arXiv  Detail & Related papers  (2023-06-14T05:48:36Z)
• Language-Guided Traffic Simulation via Scene-Level Diffusion [46.47977644226296]
  We present CTG++, a scene-level conditional diffusion model that can be guided by language instructions. We first propose a scene-level diffusion model equipped with atemporal backbone which generates realistic and controllable traffic. We then harness a large language model (LLM) to convert a users query into a loss function guiding the diffusion model towards query-compliant generation.
  arXiv  Detail & Related papers  (2023-06-10T05:20:30Z)
• Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck [77.37409441129995]
  In practical scenarios where training data is limited, many predictive signals in the data can be rather from some biases in data acquisition. We consider an adversarial threat model under a mutual information constraint to cover a wider class of perturbations in training. We propose an autoencoder-based training to implement the objective, as well as practical encoder designs to facilitate the proposed hybrid discriminative-generative training.
  arXiv  Detail & Related papers  (2023-03-24T16:03:21Z)
• PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction [78.05103666987655]
  spatial-temporal Graph Neural Network (GNN) models have emerged as one of the most promising methods to solve this problem. We propose a novel propagation delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction. Our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency.
  arXiv  Detail & Related papers  (2023-01-19T08:42:40Z)
• Deep traffic light detection by overlaying synthetic context on arbitrary natural images [49.592798832978296]
  We propose a method to generate artificial traffic-related training data for deep traffic light detectors. This data is generated using basic non-realistic computer graphics to blend fake traffic scenes on top of arbitrary image backgrounds. It also tackles the intrinsic data imbalance problem in traffic light datasets, caused mainly by the low amount of samples of the yellow state.
  arXiv  Detail & Related papers  (2020-11-07T19:57:22Z)

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.