MamNet: A Novel Hybrid Model for Time-Series Forecasting and Frequency Pattern Analysis in Network Traffic

• URL: http://arxiv.org/abs/2507.00304v1
• Date: Mon, 30 Jun 2025 22:44:07 GMT
• Title: MamNet: A Novel Hybrid Model for Time-Series Forecasting and Frequency Pattern Analysis in Network Traffic
• Authors: Yujun Zhang, Runlong Li, Xiaoxiang Liang, Xinhao Yang, Tian Su, Bo Liu, Yan Zhou,
• Abstract summary: This paper proposes a novel network traffic prediction and anomaly detection model, MamNet.<n>It integrates time-domain modeling and frequency-domain feature extraction.<n>Experiments conducted on the UNSW-NB15 and CAIDA datasets demonstrate that MamNet outperforms several recent mainstream models.
• Score: 9.37855679209798
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The abnormal fluctuations in network traffic may indicate potential security threats or system failures. Therefore, efficient network traffic prediction and anomaly detection methods are crucial for network security and traffic management. This paper proposes a novel network traffic prediction and anomaly detection model, MamNet, which integrates time-domain modeling and frequency-domain feature extraction. The model first captures the long-term dependencies of network traffic through the Mamba module (time-domain modeling), and then identifies periodic fluctuations in the traffic using Fourier Transform (frequency-domain feature extraction). In the feature fusion layer, multi-scale information is integrated to enhance the model's ability to detect network traffic anomalies. Experiments conducted on the UNSW-NB15 and CAIDA datasets demonstrate that MamNet outperforms several recent mainstream models in terms of accuracy, recall, and F1-Score. Specifically, it achieves an improvement of approximately 2% to 4% in detection performance for complex traffic patterns and long-term trend detection. The results indicate that MamNet effectively captures anomalies in network traffic across different time scales and is suitable for anomaly detection tasks in network security and traffic management. Future work could further optimize the model structure by incorporating external network event information, thereby improving the model's adaptability and stability in complex network environments.

Related papers

• Research on Cloud Platform Network Traffic Monitoring and Anomaly Detection System based on Large Language Models [5.524069089627854]
  This paper introduces a large language model (LLM)-based network traffic monitoring and anomaly detection system.<n>A pre-trained large language model analyzes and predicts the probable network traffic, and an anomaly detection layer considers temporality and context.<n>Results show that the designed model outperforms traditional methods in detection accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2025-04-22T07:42:07Z)
• MSTIM: A MindSpore-Based Model for Traffic Flow Prediction [2.4604039212534508]
  This paper proposes a multi-scale time series information modelling model MSTIM based on the Mindspore framework.<n>It integrates long and short-term memory networks (LSTMs), convolutional neural networks (CNN) and the attention mechanism to improve the modelling accuracy and stability.<n>The experimental results show that the MSTIM model achieves better results in the metrics of Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE)
  arXiv  Detail & Related papers  (2025-04-18T09:19:51Z)
• A Novel Spatiotemporal Correlation Anomaly Detection Method Based on Time-Frequency-Domain Feature Fusion and a Dynamic Graph Neural Network in Wireless Sensor Network [9.031267813814118]
  Attention-based transformers have played an important role in wireless sensor network (WSN) timing anomaly detection due to their ability to capture long-term dependencies.<n>This paper proposes a WSN anomaly detection method that integrates frequency-domain features with dynamic graph neural networks (GNN) under a designed self-encoder reconstruction framework.
  arXiv  Detail & Related papers  (2025-02-25T04:34:18Z)
• 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.<n>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)
• Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
  This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks. Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
  arXiv  Detail & Related papers  (2024-11-01T00:37:00Z)
• RACH Traffic Prediction in Massive Machine Type Communications [5.416701003120508]
  This paper presents a machine learning-based framework tailored for forecasting bursty traffic in ALOHA networks.<n>We develop a new low-complexity online prediction algorithm that updates the states of the LSTM network by leveraging frequently collected data from the mMTC network.<n>We evaluate the performance of the proposed framework in a network with a single base station and thousands of devices organized into groups with distinct traffic-generating characteristics.
  arXiv  Detail & Related papers  (2024-05-08T17:28:07Z)
• Leveraging a Probabilistic PCA Model to Understand the Multivariate Statistical Network Monitoring Framework for Network Security Anomaly Detection [64.1680666036655]
  We revisit anomaly detection techniques based on PCA from a probabilistic generative model point of view. We have evaluated the mathematical model using two different datasets.
  arXiv  Detail & Related papers  (2023-02-02T13:41:18Z)
• MAF-GNN: Multi-adaptive Spatiotemporal-flow Graph Neural Network for Traffic Speed Forecasting [3.614768552081925]
  We propose a Multi-adaptive Spatiotemporal-flow Graph Neural Network (MAF-GNN) for traffic speed forecasting. MAF-GNN introduces an effective Multi-adaptive Adjacency Matrices Mechanism to capture multiple latent spatial dependencies between traffic nodes. It achieves better performance than other models on two real-world datasets of public traffic network, METR-LA and PeMS-Bay.
  arXiv  Detail & Related papers  (2021-08-08T09:06:43Z)
• Spatio-temporal Modeling for Large-scale Vehicular Networks Using Graph Convolutional Networks [110.80088437391379]
  A graph-based framework called SMART is proposed to model and keep track of the statistics of vehicle-to-temporal (V2I) communication latency across a large geographical area. We develop a graph reconstruction-based approach using a graph convolutional network integrated with a deep Q-networks algorithm. Our results show that the proposed method can significantly improve both the accuracy and efficiency for modeling and the latency performance of large vehicular networks.
  arXiv  Detail & Related papers  (2021-03-13T06:56:29Z)
• A Generative Learning Approach for Spatio-temporal Modeling in Connected Vehicular Network [55.852401381113786]
  This paper proposes LaMI (Latency Model Inpainting), a novel framework to generate a comprehensive-temporal quality framework for wireless access latency of connected vehicles. LaMI adopts the idea from image inpainting and synthesizing and can reconstruct the missing latency samples by a two-step procedure. In particular, it first discovers the spatial correlation between samples collected in various regions using a patching-based approach and then feeds the original and highly correlated samples into a Varienational Autocoder (VAE)
  arXiv  Detail & Related papers  (2020-03-16T03:43:59Z)
• Spatial-Temporal Transformer Networks for Traffic Flow Forecasting [74.76852538940746]
  We propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) to improve the accuracy of long-term traffic forecasting. Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies. The proposed model enables fast and scalable training over a long range spatial-temporal dependencies.
  arXiv  Detail & Related papers  (2020-01-09T10:21:04Z)

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.